R-TF-015-003 Clinical Evaluation Report

Table of contents

Executive summary
- Justification of Sufficiency of Clinical Evidence
- Compliance Status Summary
Scope of the clinical evaluation
Contraindications and precautions required by the manufacturer
Clinical benefits
Data collection, model training and validation
Current knowledge - State of the Art
- Data sources for the state of the art
Clinical Evaluation of Legit.Health Plus medical device
Conclusions
Date of the next Clinical Evaluation
Qualification of the responsible evaluators
- Justification of the level of evaluators expertise
References

Executive summary

This Clinical Evaluation Report (CER) has been prepared in accordance with the requirements of Regulation (EU) 2017/745 (Medical Devices Regulation, MDR) and in line with MEDDEV guideline 2. 7/1 revision 4, Clinical evaluation: Guidance for manufacturers and notified bodies under Directives 93/42/EEC and 90/385/EEC, and MDCG 2020-1 Guidance on Clinical Evaluation (MDR) / Performance Evaluation (IVDR) of Medical Device Software as well as MDCG 2020-13 and MDCG 2020-6 as indicated in the associated Clinical Evaluation Plan (CEP).

This CER is a discussion of the benefit/risk profile of using the product Legit.Health Plus (hereinafter, "the device") to provide support to health care providers in the assessment of skin structures, enhancing efficiency and accuracy of care delivery, specifically by:

Providing quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.
Providing an interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image.

The device covers 346 validated ICD-11 categories covering visible diseases of the skin and produces three types of output: (1) a normalised probability distribution across all 346 categories for every image processed, (2) quantitative clinical sign measurements (intensity, count, and extent) for 37 clinical signs, and (3) explainability media (bounding boxes and segmentation masks). The device does not output a diagnosis, a binary result, or a treatment recommendation. Among the 346 categories, 13 are malignant neoplasms (including melanoma subtypes, basal cell carcinoma, squamous cell carcinoma, Merkel cell carcinoma, and cutaneous lymphomas) — the detailed enumeration is provided in the "Device description" section. For the complete scope, device output specifications, and malignant condition listing, see "Device description — Device outputs," "Scope of ICD-11 categories," and "High-risk and malignant conditions."

The device is classified as a class IIb medical device, and the previous version of the device (the legacy device) has been commercialized since 2020. The device is manufactured under a Conformity Assessment based on a Quality Management System in accordance with Chapter I of Annex IX of Regulation (EU) 2017/745 Medical Devices.

The clinical evaluation aimed to assess the compliance of the device with the relevant general safety and performance requirements (GSPRs), as laid down in the EU Regulation 2017/745 (MDR) (GSPR 1, 8 and 17).

The clinical evaluation of the device is mainly supported by 8 pre-market pivotal studies carried out with the frozen version of the device and 1 clinical study performed with the legacy device. The evidence portfolio spans multiple study types across the MDCG 2020-6 Appendix III evidence hierarchy; sufficiency is justified by breadth and risk-proportionate design rather than uniformity of evidence level (see "Tiered evidence assessment strategy").

The previous generation of the device, marketed since 2020 following the acquisition of its Spanish manufacturing license, has undergone continuous evaluation through post-market activities. Since the device's introduction, 21 contracts have been signed with various customers, including both government-run and for-profit care providers. Over 4,500 reports have been generated by more than 500 practitioners, benefitting over 1,000 patients. Notably, no serious incidents or Field Safety Corrective Actions (FSCA) have been reported during the review period. Furthermore, no significant trends or deviations that would necessitate corrective actions were observed in terms of the device's safety or performance.

Justification of Sufficiency of Clinical Evidence

The manufacturer has established a robust body of clinical evidence that demonstrates the safety, performance, and clinical benefit of the device, providing sufficient clinical evidence in both quantity and quality in accordance with Article 61 and Annex XIV of the Regulation (EU) 2017/745.

Quantity: The body of clinical evidence is derived from nine clinical investigations involving over 800 patients. This portfolio includes eight pivotal clinical investigations conducted with the current version of the device and one clinical investigation conducted with the equivalent legacy version. The cumulative dataset covers a broad range of dermatological conditions, user tiers (primary care practitioners and dermatologists), and clinical settings (prospective and retrospective), ensuring a statistically significant validation of the clinical performance and safety endpoints.
Quality: The clinical evidence portfolio spans multiple study types across the MDCG 2020-6 Appendix III evidence hierarchy. The highest-level individual study is MC_EVCDAO_2019, an analytical observational study (105 patients) specifically designed to assess malignancy detection performance. Real-world evidence from clinical deployment is provided by COVIDX_EVCDAO_2022, DAO_Derivación_O_2022, and DAO_Derivación_PH_2022, conducted in actual clinical settings with patients. Diagnostic accuracy improvement under controlled conditions is systematically quantified by MRMC simulated-use studies (BI_2024, PH_2024, SAN_2024, IDEI_2023, AIHS4_2025). All studies were designed following methodologically sound procedures. Pivotal investigations were pre-registered in public databases (ClinicalTrials.gov and EMA RWD Catalogue) and, where applicable, results have been published in peer-reviewed scientific literature.
Representativeness: The sufficiency of the clinical evidence regarding the intended patient population is supported by a comprehensive demographic analysis of the enrolled subjects. The study populations are representative of the target population across all life stages, including pediatric, adult, and geriatric populations, with a balanced gender distribution. Furthermore, the investigations included patients across diverse skin pigmentations (Fitzpatrick phototypes I to IV), reflecting the demographics of the intended clinical environment.
Indication Coverage: The coverage of clinical indications is justified through a risk-proportionate, tiered evidence assessment strategy. Malignant conditions (5% of dermatological presentations) are assessed with individual acceptance criteria per condition. Rare diseases are assessed as a dedicated subgroup with specific acceptance criteria. General conditions — infectious (57%), inflammatory (15%), other (19%), and vascular (1%) — are assessed as a pooled aggregate with documented risk-based justification. The 8 pivotal studies collectively cover conditions from five of the seven major epidemiological categories of dermatological disease, representing 97% of dermatological presentations (infectious 57%, other 19%, inflammatory 15%, malignant 5%, vascular 1%). Two low-prevalence categories — autoimmune (3%) and genodermatoses (1%) — have insufficient or no direct representation and are declared as acceptable gaps per MDCG 2020-6 § 6.5(e), addressed through targeted PMCF activities.
Clinical Performance and Safety: The clinical performance of the device—which directly underpins its clinical benefits—has been empirically proven against performance thresholds derived from the generally acknowledged state of the art (SotA). The safety of the device is confirmed by the absence of serious adverse events or device-related complications across all clinical investigations, supported by the extensive market experience of the equivalent legacy device (over 4,500 generated reports and zero reported serious incidents or vigilance notifications).

This robust justification, supported by high-quality clinical data and extensive market experience, confirms that sufficient evidence has been analyzed to validate the clinical benefit, safety, and performance of the device for all relevant populations and indications.

Compliance Status Summary

All General Safety and Performance Requirements (GSPRs) are met:

✓ GSPR 1 (Performance & Safety): Device achieves intended clinical performance and complies with general safety requirements
✓ GSPR 8 (Acceptability of side-effects): No serious incidents reported; acceptable risk profile
✓ GSPR 17 (Software validation): AI algorithms meet repeatability, reliability, and performance standards

On the whole, the evaluators concluded that the device complied with the general requirement on safety (GSPR 1), acceptability of side-effects (GSPR 8) and minimization of risks (GSPR 17) when used as intended by the manufacturer.

This clinical evaluation concludes that the device achieved the intended clinical performances and complies with the general requirements on performances (GSPR 1).

Based on data from risk management and observations on the device under evaluation, and considering the results obtained on the clinical performances and benefits, we were able to conclude that the device complies with the general requirements on the acceptability of the benefit/risk profile (GSPR 1, GSPR 8 and GSPR 17).

Acronyms

Acronym	Definition
CAPA	Corrective and Preventive Actions
CEP	Clinical Evaluation Plan
CER	Clinical Evaluation Report
CET	Clinical Evaluation Team
EU/EC	European Union/Community
FDA	Food and Drug Administration
FMEA	Failure Modes and Effects Analysis
FSCA	Field Safety Corrective Actions
GSPR	General Safety and Performance Requirement
IFU	Instructions For Use
MA	Metanalysis
MEDDEV	MEDical DEVices Documents
MDR	Medical Devices Regulation
MDSW	Medical Device Software
PMCF	Post-market Clinical Follow-up
PMS	Post-market Surveillance
PSUR	Periodic Safety Update Report
RCT	Randomized Controlled Trial
SotA	State of the Art
SR	Systematic Review
STED	Summary Technical Documentation
USA	United States of America

Scope of the clinical evaluation

General details

The present clinical evaluation report (CER) is intended to describe the clinical performance and safety of Legit.Health Plus (hereinafter, "the device") as a medical device software (MDSW) used for the assessment of skin structures, enhancing efficiency and accuracy of care delivery. Even though the product is currently certified as a medical device under the Medical Devices Directive 93/42/EEC (MDD) through its legacy device, this CER has been performed following the requirements of Regulation EU 2017/745 (Medical Device Regulation, MDR).

In addition to the requirements as laid in MDR, the CER has been elaborated in accordance with the guidelines and standards listed in section Applicable standards and guidance documents. Also, the CER follows the procedure GP-015 Clinical Evaluation of our QMS.

Clinical Evaluation Plan

The technical documentation relating to the clinical evaluation includes a clinical evaluation plan (CEP) and a clinical evaluation report (CER). However, as stated in sections 11 and A9 of the MEDDEV 2.7/1 rev4, the clinical evaluation report should include a section to describe the scope (stage 0 of the clinical evaluation) and, as mentioned in section 6.3 of the MEDDEV 2.7/1 rev4, the scope of the clinical evaluation is “also referred to as [...] the clinical evaluation plan”. Thus, to avoid duplicating the entire content of the CEP in the CER, which would have no interest and would be non-qualitative, the scope of the clinical evaluation is only presented in the clinical evaluation plan and this document is made available in the CER. Please refer to (R-TF-015-001 Clinical Evaluation Plan and R-TF-015-011 State of the Art Legit.Health Plus) for further details.

Objectives of the Clinical Evaluation Report

To promote a common approach for the clinical evaluation of medical devices, the European Commission published guidance whose latest version was released in 2016 (MEDDEV 2.7/1 revision 4). According to these guidelines, the “clinical evaluation report is an element of the technical documentation of a medical device” that “summarizes and draws together the evaluation of all the relevant clinical data documented or referenced in other parts of the technical documentation”. In other words, the purpose of this clinical evaluation report is to document all the information used and the conclusions made during the clinical evaluation. This notably includes the assessment of the conformity of the medical devices with the general safety and performance requirements set out in Annex I of the EU Regulation 2017/745 on Medical Devices.

As mentioned in Article 61, paragraph 1, of the EU Regulation 2017/745 on Medical Devices, ”confirmation of conformity with relevant general safety and performance requirements set out in Annex I under the normal conditions of the intended use of the device, and the evaluation of the undesirable side-effects and the acceptability of the benefit-risk- ratio referred to in Sections 1 and 8 of Annex I, shall be based on clinical data providing sufficient clinical evidence [...]”.

In other words, the conclusions of the clinical evaluation need to support the following specific General Safety and Performance Requirements (GSPR):

Specific requirements on performance of the device (GSPR 1 and GSPR 17.1)

GSPR 1: “Devices shall achieve the performance intended by their manufacturer and shall be designed and manufactured in such a way that, during normal conditions of use, they are suitable for their intended purpose”.
GSPR 17.1: “Devices that incorporate electronic programmable systems, including software, or software that are devices in themselves, shall be designed to ensure repeatability, reliability and performance in line with their intended use. In the event of a single fault condition, appropriate means shall be adopted to eliminate or reduce as far as possible consequent risks or impairment of performance”.

Specific requirements on safety (GSPR 1 and GSPR 8)

GSPR 1: “They [devices] shall be safe and effective and shall not compromise the clinical condition or the safety of patients, or the safety and health of users or, where applicable, other persons, provided that any risks which may be associated with their use constitute acceptable risks when weighed against the benefits to the patient and are compatible with a high level of protection of health and safety, taking into account the generally acknowledged state of the art”.
GSPR 8: “All known and foreseeable risks, and any undesirable side-effects, shall be minimized and be acceptable when weighed against the evaluated benefits to the patient and/or user arising from the achieved performance of the device during normal conditions of use”.

The table of contents of this CER complies with the table of contents proposed in Appendix A9 of the MEDDEV 2.7/1 rev4 guide about “How is a clinical evaluation performed”. Since the EU Regulation 2017/745 on Medical Devices does not provide any contrary information, this structure can still be used.

Qualification of the responsible evaluator(s)

The qualification requirements for the evaluators involved in this clinical evaluation are based on the guidelines in section 6.4 of MEDDEV 2.7/1 rev.4. This standard is applied in the absence of superseding requirements within the EU Regulation 2017/745 (MDR).

As stated in section "Objectives of the Clinical Evaluation Report", the clinical evaluation report also follows the structure mandated by Appendix A9 of MEDDEV 2.7/1 rev.4. This format requires that the qualifications of the responsible evaluators, along with their declarations of interest, are documented directly within the report. This information is located in ANNEX I: CV AND DECLARATIONS OF INTEREST.

Methodology

The present CER is based on the clinical evaluation of the available clinical data related to the device under evaluation as required by MDR. To this end, all relevant clinical data related to the device has been collected, appraised, and analysed following MEDDEV 2.7/1 rev. 4. The requirements for clinical evaluation are outlined in Article 61 of the MDR (including Annex XIV).

All relevant clinical data has been collected and appraised in order to establish the safety and performance of the device and to identify any gaps in clinical evidence to support the benefit/risk profile of the device. Details on the followed methodology can be found in the CEP (R-TF-015-001 Clinical Evaluation Plan).

MEDDEV 2.7.1 Rev 4: Clinical Evaluation Stages

In accordance with MEDDEV 2.7.1 Rev 4 Section 11, the clinical evaluation was conducted following the staged approach required for a complete and valid clinical evaluation:

Stage 0 (Scope of the clinical evaluation): Defined in the Clinical Evaluation Plan (R-TF-015-001), which establishes the intended purpose, device description, identification of applicable GSPRs, clinical performance parameters, clinical evaluation methodology, and literature search protocol in accordance with MEDDEV 2.7.1 Rev 4 Sections 6 and 7 and Annex A5.
Stage 1 (Identification of pertinent data, MEDDEV 2.7.1 Rev 4 §8): All pre-market and post-market clinical data relevant to the device were identified. Manufacturer-held data (pivotal clinical investigations, PMS data from the equivalent legacy device) is documented in the section "Manufacturer's clinical data." Literature data were identified through a systematic search of MEDLINE/PubMed and Cochrane CENTRAL, documented in the section "Methodology of the literature search for the device" and in the State of the Art document (R-TF-015-011).
Stage 2 (Appraisal of pertinent data, MEDDEV 2.7.1 Rev 4 §9): Each identified data set was appraised for methodological quality, relevance, and weighting. The appraisal methodology and scores (CRIT1-7 framework) are documented in the State of the Art document (R-TF-015-011). Appraisal of manufacturer-held clinical investigations is documented in the section "Manufacturer's clinical data."
Stage 3 (Analysis of clinical data, MEDDEV 2.7.1 Rev 4 §10): The appraised data were collectively analysed to determine whether they demonstrate conformity with the applicable GSPRs (1, 8, and 17). The analysis is documented in the sections "Achievement of the intended performances," "Safety," and "Assessment of the benefit/risk profile." MDR GSPRs are used in place of MDD Essential Requirements per the MDCG 2020-6 substitution rule endorsed in MEDDEV 2.7.1 Rev 4 Section 10.
Stage 4 (Writing the clinical evaluation report): This document constitutes Stage 4. It summarises all data identified, appraised, and analysed in Stages 0–3 and draws conclusions on conformity with the applicable GSPRs.

Applicable standards and guidance documents

The applicable standards and guidance documents to the present CER are listed below:

MDR 2017/745: Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices
MEDDEV 2.7/1 revision 4: European Commission Guidelines on Medical Devices Clinical Evaluation
IMDRF/AE WG/N43FINAL:2020: IMDRF terminologies for categorized Adverse Event Reporting (AER): terms, terminology structure and codes
MDCG 2023-3: Questions and Answers on vigilance terms and concepts as outlined in the Regulation (EU) 2017/745 on medical devices
IMDRF MDCE WG/N57FINAL:2019: Clinical investigation
MDCG 2024-5 Guidance on content of the Investigator's Brochure for clinical investigations of medical devices
MDCG 2024-3 Guidance on content of the Clinical Investigation Plan for clinical investigations of medical devices
2023/C 163/06: Commission Guidance on the content and structure of the summary of the clinical investigation report
MDCG 2020-10/1 Rev.1
MDCG 2020-10/2 Rev. 1: Guidance on safety reporting in clinical investigations
MDCG 2020-1: Guidance on clinical evaluation (MDR) / Performance evaluation (IVDR) of medical device software
MDCG 2020-6: Regulation (EU) 2017/745: Clinical evidence needed for medical devices previously CE marked under Directives 93/42/EEC or 90/385/EEC
MDCG 2022-21: Guidance on Periodic Safety Update Report (PSUR) according to Regulation (EU) 2017/745 (MDR)
MDCG 2020-7: Guidance on PMCF plan template
MDCG 2020-8: Guidance on PMCF evaluation report template
IMDRF MDCE WG/N65FINAL:2021: Post-Market Clinical Follow-Up Studies
MDCG 2020-13: Clinical evaluation assessment report template
IMDRF MDCE WG/N56FINAL:2019: Clinical evaluation
IMDRF MDCE WG/N55 FINAL:2019: Clinical evidence
ISO 13485:2016, Adm 11: Quality Management Systems - Regulatory Requirements for Medical Devices
ISO 14971:2019: Medical devices - Application of Risk Management to Medical Devices
ISO 14155:2020: Clinical Investigation on Medical devices for human subjects - Good clinical practice
EN 62304-1:2021: Medical device software - Software life cycle processes - Part 1: Guidance on the application of ISO 62304
ISO/IEC 62366-1:2015: Medical devices - Part 1: Application of usability engineering to medical devices
ISO 15223-1:2021: Medical devices - Symbols to be used with medical device labels, labelling and information to be supplied - Part 1: General requirements
EN 82304-2:2021: Medical device software - Software life cycle processes - Part 2: Guidance on the application of ISO 62304 to medical device software in the context of IEC 80001-1

Device description

Manufacturer

	Manufacturer data
Legal manufacturer name	AI Labs Group S.L.
Address	Street Gran Vía 1, BAT Tower, 48001, Bilbao, Bizkaia (Spain)
SRN	ES-MF-000025345
Person responsible for regulatory compliance	Alfonso Medela, Saray Ugidos
E-mail	office@legit.health
Phone	+34 638127476
Trademark	Legit.Health
Authorized Representative	Not applicable (manufacturer is based in EU)

Device identification

	Information
Device name	Legit.Health Plus (hereinafter, the device)
Model and type	NA
Version	1.1.0.0
Basic UDI-DI	8437025550LegitCADx6X
Certificate number (if available)	MDR 792790
EMDN code(s)	Z12040192 (General medicine diagnosis and monitoring instruments - Medical device software)
GMDN code	65975
EU MDR 2017/745	Class IIb
EU MDR Classification rule	Rule 11
Novel product (True/False)	TRUE
Novel related clinical procedure (True/False)	TRUE
SRN	ES-MF-000025345

Intended use

an interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image
quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others

Quantification of intensity, count and extent of visible clinical signs

The device provides quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others; including, but not limited to:

erythema,
desquamation,
induration,
crusting,
xerosis (dryness),
swelling (oedema),
oozing,
excoriation,
lichenification,
exudation,
wound depth,
wound border,
undermining,
hair loss,
necrotic tissue,
granulation tissue,
epithelialization,
nodule,
papule
pustule,
cyst,
comedone,
abscess,
hive,
draining tunnel,
non-draining tunnel,
inflammatory lesion,
exposed wound, bone and/or adjacent tissues,
slough or biofilm,
maceration,
external material over the lesion,
hypopigmentation or depigmentation,
hyperpigmentation,
scar,
scab,
spot,
blister

Image-based recognition of visible ICD categories

The device is intended to provide an interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image.

Device description

The device is a computational software-only medical device leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures. Its principal function is to provide a wide range of clinical data from the analyzed images to assist healthcare practitioners in their clinical evaluations and allow healthcare provider organisations to gather data and improve their workflows.

The generated data is intended to aid healthcare practitioners and organizations in their clinical decision-making process, thus enhancing the efficiency and accuracy of care delivery.

The device should never be used to confirm a clinical diagnosis. On the contrary, its result is one element of the overall clinical assessment. Indeed, the device is designed to be used when a healthcare practitioner chooses to obtain additional information to consider a decision.

Intended medical indication

The device is indicated for use on images of visible skin structure abnormalities to support the assessment of all diseases of the skin incorporating conditions affecting the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).

Intended patient population

The device is intended for use on images of skin from patients presenting visible skin structure abnormalities, across all age groups, skin types, and demographics.

Intended user

The medical device is intended for use by healthcare providers to aid in the assessment of skin structures.

User qualifications and competencies

This section outlines the qualifications and competencies required for users of the device to ensure its safe and effective use. It is assumed that all users already possess the baseline qualifications and competencies associated with their respective professional roles.

Healthcare professionals

No additional official qualifications are required for healthcare professionals (HCPs) to use the device. However, it is recommended that HCPs possess the following competencies to optimize device utilization:

Proficiency in capturing high-quality clinical images using smartphones or equivalent digital devices.
Basic understanding of the clinical context in which the device is applied.
Familiarity with interpreting digital health data as part of the clinical decision-making process.

The device may be used by any healthcare professional who, by virtue of their academic degree, professional license, or recognized qualification, is authorized to provide healthcare services. This includes, but is not limited to:

Medical Doctors (MD, MBBS, DO, Dr. med., or equivalent)
Registered Nurses (RN, BScN, MScN, Dipl. Pflegefachfrau/-mann, or equivalent)
Nurse Practitioners (NP, Advanced Nurse Practitioner, or equivalent)
Physician Assistants (PA, or equivalent roles such as Physician Associate in the UK/EU)
Dermatologists (board-certified, Facharzt für Dermatologie, or equivalent)
Other licensed or registered healthcare professionals as recognized by local, national, or European regulatory authorities

Each HCP must hold the academic title, degree, or professional registration that confers their status as a healthcare professional in their jurisdiction, whether in the United States, Europe, or other regions where the device is provided.

IT professionals

IT professionals are responsible for the technical integration, configuration, and maintenance of the medical device within the healthcare organization's information systems.

No specific official qualifications are mandated. Nevertheless, it is advisable that IT professionals involved in the deployment and support of the device have the following competencies:

Foundational knowledge of the HL7 FHIR (Fast Healthcare Interoperability Resources) standard and its application in healthcare data exchange.
Ability to interpret and manage the device's data outputs, including integration with electronic health record (EHR) systems.
Understanding of healthcare data privacy and security requirements relevant to medical device integration, including GDPR (Europe), HIPAA (US), and other applicable local regulations.
Experience with troubleshooting and supporting clinical software in a healthcare environment.
Familiarity with IT standards and best practices for healthcare, such as ISO/IEC 27001 (Information Security Management) and ISO 27799 (Health Informatics—Information Security Management in Health).

IT professionals may include, but are not limited to:

Health Informatics Specialists (MSc Health Informatics, or equivalent)
Clinical IT System Administrators
Healthcare Integration Engineers
IT Managers and Project Managers in healthcare settings
Software Engineers and Developers specializing in healthcare IT
Other IT professionals with relevant experience in healthcare environments, as recognized by local, national, or European authorities

Each IT professional should possess the relevant academic degree, professional certification, or demonstrable experience that qualifies them for their role in the healthcare organization, in accordance with the requirements of the United States, Europe, or other regions where the device is provided.

Use environment

The device is intended to be used in the setting of healthcare organisations and their IT departments, which commonly are situated inside hospitals or other clinical facilities.

The device is intended to be integrated into the healthcare organisation's system by IT professionals.

Operating principle

The device is computational medical tool leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures.

Body structures

The device is intended to use on the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).

In fact, the device is intended to use on visible skin structures. As such, it can only quantify clinical signs that are visible, and distribute the probabilities across ICD categories that are visible.

Explainability

For visual signs that can be quantified in terms of count and extent, the underlying models not only calculate a final value, such as the number of lesions, but also determine their locations within the image. Consequently, the output for these visual signs is accompanied by additional data, which varies depending on whether the quantification involves count or extent.

Count. When a visual sign is quantifyed by counting, the device generates bounding boxes for each detected entity. These bounding boxes are defined by their x and y coordinates, as well as their height and width in pixels.
Extent. When a visual sign is quantifyed by its extent, the device outputs a mask. This mask, which is the same size as the image, consists of 0's for pixels where the visual sign is absent and 1's for pixels where it is present.

The explainability output can be found with the explainabilityMedia key. Here is an example:

{
  "explainabilityMedia": {
    "explainabilityMedia": {
      "content": "base 64 image",
      "detections": [
        {
          "confidence": 98,
          "label": "nodule",
          "p1": {
            "x": 202,
            "y": 101
          },
          "p2": {
            "x": 252,
            "y": 154
          }
        },
        {
          "confidence": 92,
          "label": "pustule",
          "p1": {
            "x": 130,
            "y": 194
          },
          "p2": {
            "x": 179,
            "y": 245
          }
        }
      ]
    }
  }
}

Device outputs

The device produces three categories of output for every image processed. These outputs are identical regardless of the condition depicted in the image; the device does not operate in condition-specific modes.

Output 1: ICD-11 probability distribution

For each image, the device outputs a normalised probability vector across all 346 validated ICD-11 categories covering visible diseases of the skin. Each element represents the estimated probability that the image depicts a condition belonging to that ICD-11 category. The probabilities sum to 1.0. The device always outputs the full distribution across all 346 categories — it does not select, filter, or suppress any categories based on the image content. This probabilistic output is fundamentally different from a diagnostic test that provides a binary positive/negative result for a specific condition. The device provides an array of ICD-11 categories with distributed probabilities, and the clinical decision remains with the healthcare professional.

Output 2: Clinical sign measurements

The device provides quantitative measurements for 37 clinical signs using three measurement methods:

Intensity (continuous scale, 0–10): erythema, desquamation, induration, crusting, xerosis, swelling, oozing, excoriation, lichenification, exudation, wound depth, wound border, undermining, necrotic tissue, granulation tissue, epithelialization, maceration, slough or biofilm, hypopigmentation or depigmentation, hyperpigmentation, scar, external material over the lesion.
Count (integer, with bounding boxes): nodule, papule, pustule, cyst, comedone, abscess, hive, draining tunnel, non-draining tunnel, inflammatory lesion, scab, spot, blister.
Extent (cm² or percentage of affected area, with segmentation masks): hair loss; exposed wound, bone and/or adjacent tissues.

Output 3: Explainability media

For count-based signs, the device outputs bounding boxes identifying the location of each detected structure. For extent-based signs, the device outputs segmentation masks delineating the affected area. These visual overlays allow the healthcare professional to verify the basis of the quantitative measurements.

The device does not output a diagnosis, a binary positive/negative result, a treatment recommendation, a referral decision, or a prognosis. The device output is one element of the overall clinical assessment; the healthcare professional must consider the patient's medical history and other clinical findings before reaching a clinical decision.

Scope of ICD-11 categories

The device covers 346 validated ICD-11 categories covering visible diseases of the skin. These categories span multiple ICD-11 chapters — primarily chapter 14 (Diseases of the skin), chapter 2 (Neoplasms) for malignant and pre-malignant conditions, and chapter 1 (Certain infectious or parasitic diseases) for cutaneous infections, among others. The categories were derived from the device's training dataset through the mapping process documented in R-TF-028-004 Data Annotation Instructions — ICD-11 Mapping. The mapping consolidates visually indistinguishable conditions into single "Visible ICD-11 category" targets (for example, contact dermatitis and atopic dermatitis are consolidated into "Eczematous dermatitis" because they cannot be reliably differentiated by visual appearance alone). The complete list of 346 categories and their ICD-11 code mappings is maintained in R-TF-028-004.

The 346 categories collectively span the full breadth of dermatological practice. Organised by epidemiological category (Karimkhani et al. 2017, based on the Global Burden of Disease Study), the distribution is:

Epidemiological category	Global burden	Examples of conditions in the probability distribution
Infectious diseases	~57%	Tinea (corporis, pedis, capitis, versicolor), herpes simplex, herpes zoster, bacterial cellulitis, impetigo, scabies, molluscum contagiosum, cutaneous leishmaniasis, verruca vulgaris
Other conditions	~19%	Acne vulgaris, alopecia areata, vitiligo, urticaria, keloid, miliaria, contact dermatitis (irritant), androgenetic alopecia, prurigo nodularis
Inflammatory diseases	~15%	Psoriasis (vulgaris, guttate, pustular), eczema, atopic dermatitis, seborrhoeic dermatitis, lichen planus, rosacea, pityriasis rosea, granuloma annulare
Malignant and pre-malignant neoplasms	~5%	Cutaneous melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, Merkel cell carcinoma — see full enumeration below
Autoimmune diseases	~3%	Pemphigus (vulgaris, foliaceus), bullous pemphigoid, lupus erythematosus (cutaneous), dermatomyositis, morphea
Vascular conditions	~1%	Leukocytoclastic vasculitis, IgA vasculitis, livedoid vasculopathy, venous ulcer, chronic arterial occlusive disease
Genodermatoses	~1%	Ichthyosis, epidermolysis bullosa, Darier disease, neurofibromatosis

The condition examples listed above are illustrative, not exhaustive. The complete 346-category list is documented in R-TF-028-004. The global burden percentages are derived from Karimkhani et al. (2017) "The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions" and represent the proportion of global dermatological consultations attributable to each category; they do not represent the proportion of the 346 categories.

High-risk and malignant conditions

BSI specifically requested "the comprehensive list of the specific malignant/high risk diseases" within the device's scope. Among the 346 ICD-11 categories in the probability distribution, the following are clinically classified as malignant neoplasms:

Condition	ICD-11 code
Cutaneous melanoma	2C30
Acral lentiginous melanoma	2C30.3
Amelanotic malignant melanoma	2E63.00
Basal cell carcinoma	2C32
Squamous cell carcinoma	2C31
Merkel cell carcinoma	2C34
Adnexal carcinoma	2C33
Cutaneous T-cell lymphoma	2B0Z
Mycosis fungoides	2B01
Pleomorphic T-cell lymphoma	2B0Y
Dermatofibrosarcoma protuberans	2B53.Y
Angiosarcoma	2B56.1
Metastatic malignant neoplasm involving skin	2E08

In addition, the device covers pre-malignant conditions (actinic keratosis, Bowen disease), neoplasms of uncertain behaviour (ICD-11 2D41), and high-risk non-malignant conditions that require urgent clinical assessment, including Stevens-Johnson syndrome/toxic epidermal necrolysis (EB13), erythroderma, drug eruptions, bacterial cellulitis, and dissecting cellulitis. The complete list of conditions in each group is documented in R-TF-028-004.

Clinical benefit 7GH (see Section "Clinical benefits") encompasses diagnostic accuracy across all presentation types, including a dedicated sub-criterion for lesions suspicious for skin cancer (measured by AUC). The malignancy sub-criterion specifically validates that the probability distribution, when presented to healthcare professionals, improves their diagnostic accuracy for lesions suspicious for skin cancer, including the malignant neoplasm categories listed above. The device does not independently diagnose malignancy; clinical assessment and specialist referral decisions remain with the healthcare professional.

Contraindications and precautions required by the manufacturer

Contraindications

We advise not to use the device if:

Skin structures located at a distance greater than 1 cm from the eye, beyond the optimal range for examination.
Skin areas that are obscured from view, situated within skin folds or concealed in other manners, making them inaccessible for camera examination.
Regions of the skin showcasing scars or fibrosis, indicative of past injuries or trauma.
Skin structures exhibiting extensive damage, characterized by severe ulcerations or active bleeding.
Skin structures contaminated with foreign substances, including but not limited to tattoos and creams.
Skin structures situated at anatomically special sites, such as underneath the nails, requiring special attention.
Portions of skin that are densely covered with hair, potentially obstructing the view and hindering examination.

Precautions

To use the device safely, please consider the following precautions:

The device must always be used by a HCP, who should confirm or validate the output of the device considering the medical history of the patient, and other possible symptoms they could be suffering, especially those that are not visible or have not been supplied to the device.
The device must be used according to its intended use.
Before using the device, please read the Instructions for Use.

Warnings

In case of observing an incorrect operation of the device, notify us as soon as possible. You can use the email support@legit.health. We, as manufacturers, will proceed accordingly. Any serious incident should be reported to Legit.Health, as well as to the national competent authority of the country.

Undesirable effects

Any undesirable side-effect should constitute an acceptable risk when weighed against the performances intended.

It is not known or foreseen any undesirable side-effects specifically related to the use of the software.

Instructions for Use

The IFU of the device are developed according to the applicable requirement of MDR 2017/745, Annex I. As indicated in the IFU document, the use methodology is as follows:

The device is architected to seamlessly integrate with other software platforms. Primarily designed as an Application Programming Interface (API), it allows healthcare organizations to establish a real-time connection between their native systems, such as Electronic Medical Records (EMR) systems, and the device. This ensures that images can be sent from the EMR and clinical data from the device can be received and stored back into the EMR in real time.

Our instructions for use can be found at Legit.Health Plus_IFU and are made available to the users in our webpage; they contain detailed and helpful information to help our client integrate the device into their systems.

The IFU include relevant information such as intended use, warnings or contra-indications, which have been included in the Device identification section above.

Components

The device is a computational software-only medical device leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures.

Variants

No variants.

Accessories of the product

Primary accessories are the components that interact directly with the device. These can be known by the manufacturer. They are also required to interact with the device. The device is used through an API (Application Programming Interface). This means that the interface is coded, and used programmatically, without a user interface. In other words: the device is used server-to-server, by computer programs. Thus, no accessory is used directly in interaction with the device.
Secondary accessories are the components that may interact indirectly with the device. These are developed and maintained independently by the user, and the manufacturer has no visibility as to their identity or operating principles. They are also optional and not required to interact with the device.

The device may also be used indirectly through applications, such as the care provider's Electronic Health Records (EHR). The EHR is the software system that stores patients' data: medical and family history, laboratory and other test results, prescribed medications history, and more. This is developed and maintained independently of us, and may be used to indirectly interact with the device.

The healthcare providers may use image capture devices to take photos of skin structures. In this regard, the minimum requirement is a 12 MP camera.

Device materials in contact with patient or user

Due to the nature of the device (stand-alone software), it does not come into contact with tissue or bodily fluids.

Technical specifications

API REST

Our device is built as an API that follows the REST protocol.

This protocol totally separates the user interface from the server and the data storage. Thanks to this, REST API always adapts to the type of syntax or platforms that the user may use, which gives considerable freedom and autonomy to the user. With a REST API, the user can use either PHP, Java, Python or Node.js servers. The only thing is that it is indispensable that the responses to the requests should always take place in the language used for the information exchange: JSON.

OpenAPI Specification

Our medical device includes an OpenAPI Specification.

OpenAPI Specification (formerly known as Swagger Specification) is an API description format for REST APIs. An OpenAPI file allows you to describe a entire API, including:

Available endpoints and operations on each endpoint (GET, POST)
Operation parameters Input and output for each operation
Authentication methods
Contact information, license, terms of use and other information requested by the MDR regarding the label information and information to be supplied by the manufacturer.

This means that our API itself has embedded specifications that help the user understand the type of values that are transmitted by the API.

HL7 FHIR

FHIR is a standard for health care data exchange, published by HL7®. FHIR is suitable for use in a wide variety of contexts: mobile phone apps, cloud communications, EHR-based data sharing, server communication in large institutional healthcare providers, and much more.

FHIR solves many challenges of data interoperability by defining a simple framework for sharing data between systems.

The relevant performance attributes of the devices are described in the following table.

Metric	Value
Weight	33 kilobytes
Average response time	1400 miliseconds
Maximum requests per second	No limit
Service availability time slot	The service is available at all times
Service availability rate during its working slot (in % per month)	100%
Maximum application recovery time in the event of a failure (RTO/AIMD)	6 hours
Maximum data loss in the event of a fault (none, current transaction, day, week, etc.) (RPO/PDMA)	None
Maximum response time to a transaction	10 seconds
Backup device (software, hardware)	Software (AWS S3)
Backup frequency	12 hours
Backup modality	Incremental
Recomended dimensions of images sent	10,000px2

How the device achieves its intended purpose

Principle of operation

The device is a computational medical tool leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures.

Mode of action

One core feature of the device is a deep learning-based image recognition technology for the recognition of ICD categories. In other words: when the device is fed an image or a set of images, it outputs an interpretative distribution representation of possible International Classification of Diseases (ICD) categories that might be represented in the pixels content of the image.

The device makes its prediction entirely based on the visual content of the images, with no additional parameters.

The device has been developed following an architecture called Vision Transformer (ViT). This architecture is inspired in the Transformer architecture, which is extensively used in other areas such as NLP and has brought significant advancements in terms of performance.

Another core feature of the device is to provide a quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.

To achieve that, the device uses a range of deep learning technologies, combined and developed for that specific use. Here's a list of the technologies used:

Object detection: used to count clinical signs such as hives, papules or nodules.
Semantic segmentation: used to determine the extent of clinical signs such as hair loss or erythema.
Image recognition: used to quantify the intensity of visual clinical signs like erythema, excoriation, dryness, lichenification, oozing, and edema.

Glossary and Definitions of Metrics

Metric Traceability to State of the Art (SotA)

In accordance with MEDDEV 2.7/1 Rev 4 and MDCG 2020-1, the metrics used to evaluate the clinical performance and clinical benefits of the device must be firmly grounded in accepted clinical practice. The following table provides explicit traceability demonstrating that every metric used in this Clinical Evaluation Report is widely recognized and utilized in peer-reviewed scientific literature to assess diagnostic performance in dermatology, particularly for AI-guided medical devices.

Metric	Traceability to SotA References
Area under the ROC curve (AUC)	Haenssle et al. 2018, Chen et al. 2024, Han et al. 2020, Brinker et al. 2019, Tepedino et al. 2024, Ferris et al. 2025, Marchetti et al. 2019, Phillips et al. 2019, Nadour et al. 2025
Sensitivity and Specificity	Haenssle et al. 2018, Chen et al. 2024, Han et al. 2020, Brinker et al. 2019, Tepedino et al. 2024, Ferris et al. 2025, Marchetti et al. 2019, Phillips et al. 2019, Maron et al. 2019, Barata et al. 2023, Maron et al. 2020, Ahadi et al. 2021, Tschandl et al. 2019, Nadour et al. 2025
Accuracy in malignancy detection	Maron et al. 2020, Marchetti et al. 2019, Haenssle et al. 2018
Positive and Negative predictive Values (PPV/NPV)	Ahadi et al. 2021, Tepedino et al. 2024, Tschandl et al. 2019, Han et al. 2020
Top-1, Top-3 and Top-5 accuracy	Han et al. 2020, Navarrete-Dechent et al. 2021, Han et al. 2022, Jain et al. 2021, Kim et al. 2022, Muñoz-López et al. 2021, Escalé-Besa et al. 2023, Ba et al. 2022, Ferris et al. 2025, Fujisawa et al. 2018, Liu et al. 2020
Percentage of variation of accuracy, sensitivity and specificity (AI-aided)	Ba et al. 2022, Ferris et al. 2025, Fujisawa et al. 2018, Goya et al. 2020, Han et al. 2020, Han et al. 2022, Jain et al. 2021, Kim et al. 2022, Krakowski et al. 2024, Maron et al. 2020, Tschandl et al. 2020
Unaided baseline sensitivity, specificity and accuracy	Ba et al. 2022, Ferris et al. 2025, Fujisawa et al. 2018, Goya et al. 2020, Han et al. 2020, Han et al. 2022, Krakowski et al. 2024, Maron et al. 2020, Tschandl et al. 2020, Li et al. 2023
Unweighted Cohen's Kappa	Landis & Koch 1977
Percentage of reduction of unnecessary referrals	Baker et al. 2022, Eminović et al. 2009, Jain et al. 2021, Knol et al. 2006
Impact on waiting times	Giavina-Bianchi et al. 2020, Morton et al. 2010, Hsiao & Oh 2008, Spanish SNS Report 2025, DREES 2018, DERMAsurvey 2013
Remote care capacity / Teledermatology	Giavina-Bianchi et al. 2020, Orekoya et al. 2021, Kheterpal et al. 2023, Whited 2015

Metric Definitions

Top-K accuracy: An AI metric measuring how frequently the correct diagnosis appears within the top K predictions. It can apply to the device alone or to practitioners aided by the device.

Top-1 accuracy: Successful only if the single top-ranked prediction exactly matches the correct diagnosis. (When literature references general diagnostic "Accuracy," it maps to Top-1 here.) Benchmarks the algorithm's absolute precision against the primary diagnosis made by clinicians.
Top-3 / Top-5 accuracy: Successful if the correct diagnosis appears anywhere within the top three or five predictions. Reflects the real-world clinical workflow of generating a differential diagnosis — dermatology often involves visually similar conditions requiring a ranked list rather than a single definitive answer prior to biopsy.

AUC (Area Under the Receiver Operating Characteristic Curve): Measures the model's ability to distinguish between classes (e.g., malignant vs. benign) across all classification thresholds. Provides a robust measure of diagnostic discrimination power independent of the final clinical operating point, used primarily for malignancy detection evaluation.

Sensitivity and Specificity: Sensitivity measures the ability to correctly identify true positives; specificity measures the ability to correctly identify true negatives. Sensitivity is critical where minimizing false negatives is paramount (malignancy detection, referral prioritization); specificity ensures the healthcare system is not overwhelmed by false positive alerts.

PPV (Positive Predictive Value): The proportion of positive results that are true positives. Indicates the probability that a patient flagged as high-risk actually has the condition, which is relevant to avoiding unnecessary clinical follow-ups.

NPV (Negative Predictive Value): The proportion of negative results that are true negatives. Critical for malignancy screening — it indicates the probability that a patient classified as low risk is truly free of the condition.

ICC (Intraclass Correlation Coefficient): A statistical measure of reliability and agreement between raters or measurements. Used to evaluate consistency between the device's severity assessments and human expert judgments, ensuring quantitative outputs are comparable to expert clinical standards.

Unweighted Kappa (Cohen's Kappa): Measures inter-rater reliability for categorical items, accounting for agreement by chance. Used to assess agreement between the device and clinical experts for categorical severity levels (Mild, Moderate, Severe).

Experts' Consensus (Majority Vote): A reference standard methodology where correctness is defined by agreement among a majority of independent medical experts (typically ≥ 75%). Used for complex cases where individual expert opinions vary, benchmarking the device against a collective high-quality clinical reference.

Efficiency and Resource Optimization Metrics: Metrics quantifying the systemic impact of the device on healthcare workflows.

Reduction in Cumulative Waiting Time: Measures the decrease in total patient waiting time for specialist consultations after implementing the device's triage support. Demonstrates the benefit of optimized referral pathways by ensuring high-risk cases are seen sooner while reducing unnecessary load on specialist services.
Reduction in Unnecessary Referrals: Measures the proportion of low-risk cases managed in primary care without specialist referral, compared to unaided practice. Quantifies the device's contribution to healthcare resource optimization by supporting PCPs in identifying cases that do not require specialist intervention.

Use environment

We distinguish between the technical deployment environment and the clinical workflow modality:

Use Environment (Healthcare Facility): Refers to the technical deployment environment (integration with hospital EMRs, clinics, or professional IT infrastructure), ensuring data security and system reliability.
Clinical Modality (Remote Care/Teledermatology): Refers to the workflow in which the device is employed. The device is intended for use by HCPs within healthcare facilities to facilitate both in-person assessments and remote assessments (teledermatology), improving the efficiency of referral pathways.

Clinical benefits

For more information regarding the evaluation and qualification of claimed benefits of the device, please refer to the document Performance Claims & Clinical Benefits. The device is claimed to provide 7 distinct clinical benefits: (1) Improved diagnostic accuracy of dermatological conditions, (2) Reduction of waiting time for skin-related medical consultations, (3) Optimized referral prioritization, (4) Severity assessment support, (5) Remote consultation capability, (6) Improved diagnostic accuracy of rare dermatological conditions, and (7) Improved accuracy of HCPs during the detection of lesions suspected of malignancy.

Data collection, model training and validation

The development of the AI algorithms incorporated in the device follows the systematic approach defined in GP-028 AI Development, which establishes the methodology for data collection, model training, validation, and maintenance of AI models. The complete AI development lifecycle is documented in the AI Development Plan and AI Development Report for each version of the device.

Data Collection and Management

The data collection process is conducted in accordance with GP-028 AI Development and follows documented Data Collection Instructions (R-TF-028-003 Data Collection Instructions) that specify:

Dataset Composition: Images were collected from diverse sources, including established skin image datasets and clinical partnerships, ensuring representation of various demographics (age, sex, skin tone) and clinical presentations
Dataset Size: The dataset comprises images covering near 1000 different ICD categories, with sufficient samples per category to enable robust model training
Acquisition Protocol: Clinical and technical requirements for image acquisition were specified to ensure consistency and quality across all data sources

All data sources are documented with complete traceability, including provenance, acquisition dates, and verification of compliance with data collection requirements. Data quality verification was performed to ensure images met predefined quality standards before inclusion in the training dataset.

Data Annotation

Medical expert annotations were performed following formal Data Annotation Instructions (R-TF-028-004 Data Annotation Instructions) prepared by the AI Team in collaboration with clinical experts. These instructions provide unambiguous guidance for:

Application of ICD category labels to each image
Delineation of clinical signs (where applicable)
Annotation quality criteria

All annotators received formal training on these instructions, and annotation quality was verified through inter-annotator agreement metrics and compliance checks. Records of annotator training and competence are maintained in the Device History File.

Data Partitioning Strategy

One crucial step of the development is splitting the dataset into three independent subsets, following best practices in machine learning and the methodology defined in GP-028 AI Development:

Training set: Used to fit or train the parameters of the AI model
Validation set: Used to provide an unbiased evaluation of the model fit on the training set while tuning model hyperparameters
Test set: A fixed subset used to provide an unbiased evaluation of the final model's performance after training is complete

Subject-level splitting

When an incoming image dataset includes metadata that makes it possible to group images by subject (patient), the data is split at the subject level. This strategy prevents data leakage (where images from the same patient appear in both training and test sets) and improves the reliability of the validation and test metrics. This is recognized as a best practice in the field of medical AI.

Dataset reservation for testing

Thanks to a large collection of datasets from diverse sources, it is possible to perform robust external validation by reserving some complete datasets entirely for testing. This approach helps explore and analyze the performance of the model in completely uncontrolled scenarios, simulating real-world deployment conditions.

Model Training and Development

The model training process follows the specifications detailed in the AI Development Plan (R-TF-028-002 AI Development Plan), which defines:

Model Architecture: The device employs a Vision Transformer (ViT) architecture, inspired by the Transformer architecture extensively used in natural language processing. This architecture has demonstrated significant performance improvements in image recognition tasks
Training Configuration: Hyperparameters, loss functions, optimization algorithms, data augmentation strategies, and training procedures are specified and documented
Training Process: The methodology includes transfer learning strategies, convergence criteria, and monitoring procedures to ensure optimal model performance
Experiment Tracking: Comprehensive records of all experiments, parameter settings, and results are maintained for full reproducibility and traceability

The training process is supported by multiple deep learning technologies tailored to specific clinical tasks:

Object detection: Used to count clinical signs such as hives, papules or nodules
Semantic segmentation: Used to determine the extent of clinical signs such as hair loss or erythema
Image recognition: Used to quantify the intensity of visual clinical signs like erythema, excoriation, dryness, lichenification, oozing, and edema

Model Evaluation and Validation

Algorithm evaluation is conducted according to the metrics and acceptance criteria defined in R-TF-012-009 Validation and Testing of Machine Learning Models. The AI Development Report (R-TF-028-005 AI Development Report) documents comprehensive evidence that the model meets all acceptance criteria, including:

Performance Metrics: Detailed results for all clinically relevant metrics (e.g., sensitivity, specificity, AUC, F1-score) on the fixed test set, with statistical confidence intervals where applicable
Subgroup Analysis: Performance is evaluated across demographic subgroups (age, sex, skin tone) to identify and mitigate potential model bias
External Validation: Performance is assessed on completely independent datasets not used during development to validate generalization capability
Clinical Validation: Results from clinical studies (documented in this CER) provide evidence of the device's performance in real-world clinical settings

Commissioning and Real-World Validation

Following development and initial validation, the device undergoes commissioning activities as defined in GP-029 Software Delivery and Commissioning. The commissioning process validates the device in its intended environment of use by:

Objective 1 - Internal Validation in Representative Environments: The AI Labs team creates representative test environments that simulate how clients will integrate the API, including:
- Test mobile applications (iOS, Android) that integrate the device
- Test web applications that consume the device's API
- Simulations of EHR system integrations with FHIR data exchange
- Testing under various network conditions and authentication methods
- Validation that integration documentation is complete and accurate
Objective 2 - Client Integration Assurance: Establishment of a comprehensive support framework to ensure clients integrate the device correctly and safely:
- Complete integration documentation and code examples
- Sandbox environment for client testing
- Technical support during integration
- Monitoring of client integrations to identify issues

The commissioning activities are documented in the Software Commissioning Plan (R-TF-029-001 Software Commissioning Plan) and Software Commissioning Report (R-TF-029-002 Software Commissioning Report), which provide evidence of IEC 82304-1:2016 section 6.2 compliance by demonstrating that the software product satisfies user requirements in the intended environment of use.

Risk Management

AI-specific risks identified during development are documented in the AI Risk Matrix (R-TF-028-011 AI Risk Matrix) and communicated to the product development team for inclusion in the overall risk management file (R-TF-013-002 Risk Management Record). This ensures that risks related to data quality, model performance, and potential bias are systematically managed and mitigated.

Traceability and Documentation

Complete traceability is maintained throughout the AI development lifecycle, with all activities documented in accordance with GP-028 AI Development and GP-029 Software Delivery and Commissioning. This includes:

Dataset provenance and version control
Model architecture and training configuration
Experiment logs and results
Validation and test results
Commissioning activities and results
Post-market performance monitoring data

This comprehensive documentation ensures compliance with regulatory requirements (MDR 2017/745, IEC 82304-1:2016, IEC 62304:2006+A1:2015) and supports continuous improvement of the device through post-market surveillance activities.

Status of commercialization

This product has not been commercialized yet. It is undergoing initial CE mark.

Previous version of the device

The predecessor of the current device is named Legit.Health (hereinafter, "the legacy device"). Legit.Health Plus is an evolution of the legacy Legit.Health currently available on the market under the Medical Devices Directive (MDD). The transition from the legacy version to the "Plus" version involved minor technical updates aimed at software version stabilization, consolidation of existing features, and full alignment with the Medical Device Regulation (MDR) (EU) 2017/745 requirements. These changes have been assessed and do not impact the device's clinical safety, performance, or fundamental principles of operation. While additional clinical data has been collected to satisfy the higher level of evidence required for its reclassification as Class IIb, the intended purpose remains consistent. This approach aligns with the necessary transition from the MDD to the MDR, specifically addressing:

Updated Technical Documentation: As mandated by Article 10(4) and Annexes II and III of the MDR, demonstrating conformity with the new General Safety and Performance Requirements (GSPR).
Post-Market Clinical Follow-up (PMCF) Data: The collected clinical data serves to strengthen the Clinical Evaluation Report (CER), in line with MDR Article 61 and Annex XIV, and relevant guidance from the Medical Device Coordination Group (MDCG) (e.g., MDCG 2020-13 on clinical evaluation and PMCF).
Demonstration of Equivalence: Since the core technology and clinical application remain unchanged, the updated documentation demonstrates equivalence to the legacy device, enabling the use of existing market experience data to support the clinical evaluation.

The legacy device has been commercialized since 2020 (after obtaining the manufacturing license in Spain) and was certified under the Medical Devices Directive (MDD). Details on its market experience are integrated into Section 16.1.4.

Current knowledge - State of the Art

Data sources for the state of the art

In the context of the European Union's Medical Devices Regulation (MDR) 2017/745, state of the art refers to the current level of technical development and accepted clinical practice in products, processes and patient management. Although it lacks an explicit definition, it is understood as the consolidated state of knowledge in science and technology at a specific point in time. It does not necessarily imply the most advanced, expensive or frequently used solution, but what is currently accepted as good practice. The identification and understanding of this state is crucial for risk assessment and plays a key role in the writing of clinical evaluation reports, ensuring alignment with the intended use of medical devices and effective management of associated risks.

In relation to the current knowledge / state of the art in the relevant medical field, the following aspects and information have been verified:

Applicable standards and medical guidelines.
Information related to the medical condition managed with the device.
Other similar devices marketed and medical alternatives available for the target population.

The full state-of-the-art description is in a separate document (R-TF-015-011 State of the Art Legit.Health Plus), attached also to the Clinical Evaluation Plan (CEP) and Report (CER).

The table below summarizes the state-of-the-art data related to the device:

Aspect	Details
1. Methodological Referential for Bibliographic Search	- MedDev 2.7/1 Rev.4 (applicable guidance for clinical evaluation) - PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses
2. Type of search	Systematic (documented search strategy, screening, eligibility and selection steps; audit trail available in methods section).
3. Results (bibliographic search)	Source search yielded N = 227 candidate records. After de-duplication and multi-stage screening, n = 64 clinical articles were included and appraised for methodological quality and relevance. An additional n = 10 items (primarily two manuscripts, 8 guidelines and contextual documents) were referenced to inform clinical context; total material considered = 74. Breakdown used for appraisal: 66 clinical studies; 8 clinical guidelines; 0 unpublished trial reports; 0 registry reports.
4. Referential for data appraisal and weighting	- IMDRF MDCE WG/N56FINAL:2019 (risk-based clinical evaluation principles) - Internal appraisal templates informed by Yale and Johns Hopkins academic resources (see Methods)
5. Results (appraisal summary / mean weight)	Appraisal summary for clinical datasets (n = 64): mean weight = 6.91 / 10. Additional metrics: mean relevance = 4.62 / 6; mean quality = 2.60 / 4; mean level of clinical evidence = 6.0 / 10. Note: datasets with weight < 4 require justification in the clinical evaluation file; none of the included datasets used in the main analysis had weight < 4 without documented rationale.
6. Use	Intended use statement: AI-guided medical devices are intended as an adjunctive clinical decision support tools to assist clinicians (primary care practitioners and dermatologists) during dermatology consultation workflows for triage and diagnostic evaluation of skin conditions. It is not intended to replace clinician judgment. Target population: patients presenting with skin lesions or dermatological complaints across adult age groups. User training, labeling, and intended use constraints consistent with similar devices in the literature are required.
7. Expected complications	Observed/anticipated hazards: no direct patient harm events attributable to similar devices were identified in the reviewed clinical evidence. Principal risks to be managed: (1) reduced accuracy on heterogeneous, real-world images (dataset shift); (2) inappropriate clinician reliance on AI outputs when used without verification (automation bias); (3) false-negative results leading to missed malignancy or delayed referral; (4) false-positive results increasing unnecessary referrals/biopsies. Recommended risk controls: human-in-the-loop workflow, explicit user instructions and limitations, mandatory training, robust PMS and RCA procedures, and monitoring of real-world performance metrics.
8. Expected benefits and performances	Access to specialist dermatology services is constrained in many health systems, with variable wait times and heterogeneous diagnostic performance between primary care practitioners (PCPs) and dermatologists. The reviewed literature confirms consistent performance gaps (PCPs show lower sensitivity than dermatologists on clinical image assessments), and that dermoscopy and specialist assessment improve diagnostic accuracy. AI tools have been studied primarily as adjuncts to clinician assessment and as standalone classifiers on curated image sets; real-world performance is commonly lower than reported in controlled datasets, underscoring the need for robust external validation and post-market surveillance. - Clinical performance observed in reviewed literature: on curated dermoscopic test sets, standalone AI classifiers typically reported sensitivity in the approximate range 80-86% and specificity in the range 77-83%. High-quality meta-analytic evidence (systematic reviews) reports pooled sensitivity and specificity that are consistent with these ranges for melanoma detection using dermoscopic images; performance on clinical (unmagnified) images is lower and more variable. Comparative reader studies demonstrate that AI, when used as a diagnostic adjunct, improves clinician sensitivity and overall accuracy (for example, Maron et al. 2020 reported clinician sensitivity increase from ~59% to ~75% with AI assistance; other reader and trial studies show similar magnitude improvements in sensitivity and modest improvements in specificity or overall accuracy). - Expected clinical benefits: improved detection sensitivity for malignancy (reducing missed cancers), standardization of preliminary triage decisions, support for prioritization of referrals to secondary care, potential reduction in unnecessary specialist referrals and benign biopsies when AI is combined with clinical assessment, and increased efficiency in workflows (fewer repeat assessments, faster triage). Benefits are contingent on correct deployment: appropriate external validation, integration into clinician workflows with human oversight, and active PMS to detect performance drift. Conclusion: the evidence supports adoption as a clinician-support tool under controlled conditions and with documented risk controls; standalone use without clinician oversight is not supported by the available clinical evidence and is not recommended in the intended use statement.

Clinical Evaluation of Legit.Health Plus medical device

Tiered evidence assessment strategy

The device outputs a probability distribution over all visible ICD-11 categories simultaneously. It does not produce a binary positive/negative for any specific condition. This architecture means the device functions as a general classifier whose performance can validly be assessed across the full breadth of dermatological conditions.

However, MDR Article 61 and Annex XIV 1(a) require that the clinical evaluation demonstrate the device's clinical benefits, performance, and safety for its intended purpose. Article 2(53) defines clinical benefit in terms of a meaningful clinical outcome, and Annex II requires that the technical documentation include evidence that the device meets the applicable GSPRs for each claimed indication. Implementing these requirements, the applicable guidance documents — MEDDEV 2.7.1 Rev 4 Annex A7.3 (sensitivity/specificity for major clinical indications), MDCG 2020-1 (separate Valid Clinical Association per claimed output), and MDCG 2020-6 Appendix III (risk-based justification for data pooling) — mandate condition-level or category-level evidence assessment. Accordingly, the clinical evaluation adopts a risk-proportionate, tiered evidence structure:

Tier 1 (Malignant conditions, individual analysis): The clinical consequence of misclassification is highest: delayed cancer diagnosis can lead to disease progression and mortality. Performance is assessed with individual acceptance criteria per condition or condition group. Evidence is drawn from dedicated studies (MC_EVCDAO_2019 for melanoma, plus malignancy prediction endpoints across 6 further studies).
Tier 2 (Rare diseases, grouped analysis): Rare diseases are frequently misdiagnosed; delayed diagnosis leads to prolonged suffering and inappropriate treatment. Performance is assessed as a dedicated subgroup with its own acceptance criterion within benefit 7GH (absolute Top-1 accuracy >= 54%). The rare diseases subgroup is defined in the BI_2024 study protocol: GPP, acne conglobata, palmoplantar pustulosis, subcorneal pustular dermatosis, AGEP, and pemphigus vulgaris.
Tier 3 (General conditions, pooled with risk-based justification): For non-malignant, non-rare conditions, the clinical consequence of an incorrect ranking is comparable: delayed or modified treatment, not mortality. Performance is assessed as a pooled aggregate with explicit risk-based justification (see below).

This tiered structure ensures that evidence assessment is proportionate to clinical risk: high-risk conditions receive individual scrutiny, while lower-risk conditions are validly pooled with documented justification.

Data pooling methodology

Aggregate performance metrics (globalValueOfDevice) are calculated using a weighted average formula: Σ(achievedValue × sampleSize) / Σ(sampleSize).

Pooling of Tier 3 (general conditions) data across conditions is justified on four grounds:

Comparable clinical consequence of misclassification. Within non-malignant, non-rare categories, the typical clinical consequence of an incorrect ranking is delayed or modified treatment. While individual exceptions exist (e.g., untreated infectious conditions can occasionally progress to serious complications), the physician's independent clinical assessment — not the device output alone — determines the management pathway, providing a safety net that is absent in standalone diagnostic scenarios. This risk profile is fundamentally different from Tier 1 (malignant conditions), where a missed diagnosis directly impacts mortality.
Device architecture supports pooling. The device outputs a probability distribution over all ICD-11 categories simultaneously. It does not make independent per-condition predictions; it ranks likelihoods across the full ICD-11 space. Assessing how well this ranking performs across the general dermatological spectrum is therefore a natural and valid evaluation approach.
Representative sampling across epidemiological categories. The pooled studies include conditions from all major epidemiological categories of dermatological disease (see Evidence coverage by disease category below), ensuring that the pooled analysis reflects the breadth of conditions encountered in clinical practice rather than being limited to a single disease area.
Consistent architecture supports the expectation of consistent capability. The uniform Vision Transformer architecture processes all input images through the same feature extraction pipeline regardless of condition, introducing no condition-specific biases. While absolute performance varies by condition (as demonstrated in per-condition results tables within each study), validated capability on representative conditions from across the epidemiological spectrum provides a technical basis for confidence in the device's generalised performance.

The populations across the pooled studies were representative of real-world clinical practice, including both primary care physicians and dermatologists as intended users. The studies were conducted in comparable clinical settings with representative patient demographics, ensuring that the results are applicable to the intended population.

Evidence coverage by disease category

Throughout the clinical evaluation, several performance claims use the indication label "Multiple conditions." This label does not refer to an unspecified group of diseases. It reflects a broad, representative inclusion aligned with the diverse ICD-11 categories evaluated in the respective clinical studies. To demonstrate this representativeness, the clinical evidence portfolio is mapped against the seven major epidemiological categories of dermatological disease, based on the Global Burden of Disease Study (Karimkhani et al., 2017):

Category	Approximate prevalence	Studies with representation
Infectious diseases (fungal, bacterial, viral)	57%	BI_2024 (impetigo, tinea corporis), SAN_2024 (herpes, tinea, onychomycosis), COVIDX_EVCDAO_2022 (folliculitis, herpes, tinea), DAO_Derivación_PH_2022 (warts, molluscum, herpes simplex)
Other conditions (acne, alopecia, urticaria)	19%	BI_2024 (acne variants), SAN_2024 (acne, alopecia, urticaria), IDEI_2023 (androgenetic alopecia, 96 patients), COVIDX_EVCDAO_2022 (acne, 67 patients; alopecia), DAO_Derivación_O_2022 (alopecia), PH_2024 (urticaria), DAO_Derivación_PH_2022 (urticaria)
Inflammatory diseases (psoriasis, AD, HS, eczema)	15%	BI_2024 (GPP, dermatitis, psoriasis, HS, AGEP +4), PH_2024 (psoriasis ×2, HS), SAN_2024 (dermatitis, psoriasis), AIHS4_2025 (HS severity), COVIDX_EVCDAO_2022 (psoriasis, AD, HS, eczema, lichen planus, rosacea), DAO_Derivación_O_2022 (psoriasis ×3, eczema ×3, AD), DAO_Derivación_PH_2022 (psoriasis, AD, HS, lichen planus)
Malignant diseases (melanoma, BCC, SCC)	5%	MC_EVCDAO_2019 (melanoma 36, BCC 13, actinic keratosis), IDEI_2023 (melanoma, BCC, SCC), PH_2024 (melanoma, BCC, actinic keratosis), SAN_2024 (melanoma), DAO_Derivación_O_2022 (melanoma ×4, BCC ×9, actinic keratosis 27), DAO_Derivación_PH_2022 (BCC, SCC, melanoma), COVIDX_EVCDAO_2022 (melanoma, BCC, SCC, actinic keratosis)
Autoimmune diseases (lupus, bullous diseases)	3%	BI_2024 (pemphigus vulgaris), DAO_Derivación_O_2022 (bullous pemphigoid)
Genodermatoses (epidermolysis bullosa, ichthyosis)	1%	No direct representation in the clinical evidence portfolio
Vascular diseases (haemangiomas, malformations)	1%	MC_EVCDAO_2019 (angioma, haemangioma, angiokeratoma), COVIDX_EVCDAO_2022 (haemangioma, 14 patients), DAO_Derivación_O_2022 (spider telangiectasis, pyogenic granuloma), DAO_Derivación_PH_2022 (angiomas)

Five of the seven epidemiological categories have direct representation across multiple studies, collectively covering 97% of dermatological presentations (infectious 57%, other 19%, inflammatory 15%, malignant 5%, vascular 1%). Two categories have insufficient representation: autoimmune diseases (3%) are limited to two conditions in two studies, and genodermatoses (1%) have no direct representation. These are addressed as declared acceptable gaps with documented justification in the section Need for more clinical evidence.

Type of evaluation

As outlined in EU Regulation 2017/745 on Medical Devices (article 61, paragraph 3), “a clinical evaluation shall follow a defined and methodologically sound procedure based on the following:

A critical evaluation of the relevant scientific literature currently available relating to the safety, performance, design characteristics, and intended purpose of the device, where the following conditions are satisfied:

(i) it is demonstrated that the device subject to clinical evaluation for the intended purpose is equivalent to the device to which the data relate [...], and (ii) the data adequately demonstrate compliance with the relevant general safety and performance requirements;

a critical evaluation of the results of all available clinical investigations, [...]; and
a consideration of currently available alternative treatment options for that purpose, if any.”

In this way, this clinical evaluation is based on:

Clinical data specific to the device under evaluation
Clinical data related to an equivalent device (legacy device Legit.Health)

Demonstration of equivalence

In accordance with the MDR, the guidance document MDCG 2020-5, a detailed technical, clinical, and biological equivalence evaluation was conducted between Legit.Health Plus and the legacy device Legit.Health.

Technical equivalence

MDR 2017/745 (Annex XIV Part A (3)) specifies that in order for the device to be determined as technically equivalent to a comparator, the target device must be of similar design, be used under similar conditions of use, and have similar specifications and properties, use similar deployment methods, and have similar principles of operation and critical performance requirements. The following table 11 summarizes how the device and the legacy device are technically similar in all technical aspects.

Technical Characteristics	Legit.Health Plus	Legit.Health
Intended purpose	To assist healthcare professionals in the evaluation and monitoring of dermatological conditions through AI-driven analysis of clinical images.	Same
Indications	Dermatological conditions including acne, atopic dermatitis, psoriasis, hidradenitis suppurativa, and skin cancer suspicion.	Same
Contraindications	Not intended for use in emergency diagnosis or as a standalone diagnostic tool.	Same
Precautions	Requires good-quality clinical images and usage within intended environments.	Same
Target patient groups	Patients with visible dermatological conditions across all skin types.	Same
Target users	Healthcare professionals (e.g., general practitioners, dermatologists).	Same
Design Characteristics	Legit.Health Plus.	Legit.Health.
Overall Design	Software-only medical device with web interface and REST API integration.	Same
Type of device	Standalone software, non-invasive.	Same
Conditions of use	Online clinical environment, requires connectivity and appropriate hardware.	Same
Specifications	Legit.Health Plus.	Legit.Health.
Image analysis algorithm	AI-based (CNN models for lesion detection, segmentation, and scoring).	Same
Severity Scoring Tools	AIHS4, ALADIN, EASI, PASI (AI-based scoring systems).	Same
Output format	Structured severity reports, triage suggestions, image annotations.	Same
Properties	Legit.Health Plus.	Legit.Health.
Storage method	Cloud-hosted with secure access, encrypted data.	Same
Interfacing environment	Mobile and desktop devices via browser/API.	Same
Deployment	Legit.Health Plus.	Legit.Health.
Deployment method	Web application or API integrated in electronic health records or teledermatology platforms.	Same
Principles of Operation	Legit.Health Plus.	Legit.Health.
Preparation for use	Log-in via browser or connected system, image acquisition as per clinical protocol.	Same
Technique	Capture of lesion images, processed by AI algorithms for diagnosis support.	Same
Mode of Action	Software processes image input and outputs lesion classification, malignancy suspiction and severity data.	Same
Duration of use	Episodic per consultation (non-continuous).	Same
Critical Performance Requirements	Legit.Health Plus.	Legit.Health.
Diagnostic support	AUC ≥ 0.9 for malignancy, specificity ≥ 80%, sensitivity ≥ 75% (validated in studies).	Same
Quantification accuracy	Agreement with expert scoring standards in conditions such as acne, psoriasis, etc.	Same

Technical equivalence conclusion

The technical equivalence between the device and the legacy device is justified based on their shared core architecture, identical intended purpose, and the use of the same fundamental algorithms for image processing and clinical quantification. Both devices are software-only applications designed to support healthcare professionals in the assessment and monitoring of dermatological conditions using AI-based analysis of clinical images. The transition from Legit.Health to Legit.Health Plus did not involve significant changes in functionality, performance specifications. This confirms that the devices are technically equivalent and that the legacy data remains applicable under the MDR framework.

Clinical equivalence

MDR 2017/745 (Annex XIV Part A (3)) states that in order for devices to be determined as clinically equivalent they must be used for the same clinical condition or purpose, at the same site of use in the body, in a similar patient population, has the same kind of user, and has similar relevant critical performance in view of the expected clinical effect for a specific intended purpose. The following table provides a comparison of the clinical characteristics of Legit.Health Plus and Legit.Health.

Clinical Characteristics	Legit.Health Plus	Legit.Health	Comparison
Clinical Condition	Wide range of dermatological conditions (e.g., melanoma, acne, psoriasis, GPP, etc.)	Same range of dermatological conditions	Equivalent: both cover the same diagnostic scope based on clinical image analysis.
Intended purpose	Support clinical evaluation and monitoring by quantifying signs in dermatological images	Same intended purpose	Equivalent: both designed for AI-assisted dermatological evaluation.
Site in the body	Skin (cutaneous surface, including localized or generalized conditions)	Same	Equivalent: both focus on visible skin lesions and signs.
Patient population	General population	Same	Equivalent: both target a broad patient population
Type of user	Healthcare professionals (e.g., GPs, dermatologists) and IT professionals	Same	Equivalent: both are intended for: HCPs and IT professionals.
Critical Performance in view of the expected clinical effect	Accurate identification and quantification of clinical signs; decision support for diagnosis and monitoring	Same capabilities, based on the same core algorithm and software model	Equivalent: both aim to support early detection, severity scoring, and monitoring, with validated performance based on clinical and preclinical data.

Clinical equivalence conclusion

The clinical equivalence between the device and the legacy device is supported by their identical intended purpose, clinical indications, target patient population, and type of user. Both devices are designed to assist healthcare professionals in the evaluation and monitoring of dermatological conditions through the analysis of clinical images using artificial intelligence algorithms. They address the same range of dermatological conditions, target the same anatomical site (skin), and are intended for use by the same type of qualified users. Furthermore, both rely on the same core algorithm and software framework, ensuring that the clinical performance, including diagnostic accuracy and decision support functionality, remains consistent between versions. As such, Legit.Health Plus maintains clinical equivalence with the legacy Legit.Health device.

Biological equivalence

MDR 2017/745 (Annex XIV Part A (3)) states that in order to be determined as biologically equivalent, devices must use the same materials or substances in contact with the same human tissues or body fluids for a similar kind and duration of contact and similar release characteristics of substances, including degradation products and leachables.

In this case, biological equivalence is not applicable because the device is a software-only medical device and does not have any direct or indirect contact with the human body, tissues, or fluids. The device functions through the analysis of dermatological images captured externally, typically via smartphone cameras, and does not involve any material components that would pose a biological interaction or release of substances. Therefore, there is no biological interface that could give rise to toxicological or immunological concerns, and the requirement to establish biological equivalence is not relevant for this device category.

Conclusions regarding equivalence

Legit.Health Plus and the legacy device Legit.Health have been shown to be equivalent with respect to clinical and technical characteristics, as outlined in the corresponding equivalence tables. The two software versions share the same intended purpose, target population, type of user, core algorithm, software architecture, and performance objectives. There are no changes in the clinical condition addressed or the fundamental principles of operation. Given that both products were developed by the same manufacturer, AI Labs Group S.L., there is full access to the design, technical documentation, and performance data of both devices.

The improvements introduced in Legit.Health Plus compared to the legacy version (1.0.0.0) are limited strictly to software stabilization and feature consolidation required for MDR compliance. Specifically, the exact changes made are:

Migration to a microservices architecture: to improve server scalability and response times under high load.
Implementation of the HL7 FHIR standard: to ensure standardized, secure interoperability with hospital Electronic Health Record (EHR) systems.
Database encryption and cybersecurity upgrades: to meet state-of-the-art cybersecurity and data protection requirements under the MDR.
Enhanced user interface (UI) feedback: to provide clearer instructions and error messages when image quality is insufficient (based on the DIQA algorithm), directly mitigating usability risks identified during the transition to MDR.

Justification for Lack of Clinical Impact

None of these changes affect the core Artificial Intelligence models, the Vision Transformer architecture, the clinical indications, or the fundamental principles of operation. The mathematical algorithms that process the pixels and generate the clinical output remain completely identical to those validated in the legacy device. Therefore, these software stabilization and security updates are not expected to—and structurally cannot—negatively impact the clinical safety, clinical performance, or diagnostic accuracy of the device. On the contrary, these changes aim to facilitate conformity under the MDR by freezing the core functionality, improving overall system security, and maintaining the exact same clinical risk profile as the legacy version.

As a result of this demonstrated equivalence, previously generated clinical data for the legacy device—collected under appropriate ethical and scientific standards—are considered applicable and valid to support the clinical evaluation of Legit.Health Plus. This allows the clinical evaluation team to rely on the existing body of evidence to confirm the safety and performance of the device currently under assessment.

Justification for Additional Clinical Evidence versus the Legacy Device

The device under evaluation is an evolution of the legacy device, which was CE-marked under the previos Medical Devices Directive 93/42/EEC (MDD) and classified as Class I. While technical and functional continuity exists with the legacy device, the transition to the new regulatory framework, Regulation (EU) 2017/745 (MDR), introduces significantly more stringent requirements that directly impact the clinical evaluation strategy. The justification for generating new clinical evidence is based on two primary regulatory pillars:

Change in Risk Classification and Increased Level of Evidence Required.

Under the MDR framework, and in accordance with the classification rules stipulated in Annex VIII, the device has been reclassified as Class IIb.

This reclassification (from Class I under MDD to Class IIb under MDR) reflects a higher risk profile recognized by the new regulation. Consequently, Article 61(1) of the MDR mandates a clinical evaluation and a level of clinical evidence that are proportionate and appropriate to this higher risk class. The clinical documentation and data compiled for the Class I legacy device are not, in themselves, sufficient to satisfy the level of scrutiny required for a Class IIb device.

Conformity with the General Safety and Performance Requirements (GSPR).

The MDR replaces the "Essential Requirements" (ERs) of the MDD with the General Safety and Performance Requirements (GSPRs), detailed in Annex I of the MDR. The GSPRs are more detailed, prescriptive, and demanding, particularly regarding clinical validation, risk management, and usability. For example, the GSPRs require a more robust quantification of clinical benefits (GSPR 1), and specific requirements for software validation (GSPR 17), which were not defined with the same rigour under the MDD.

Conclusion

Although data from the legacy device are used as fundamental supporting evidence, these data alone create an "evidence gap" when measured against the requirements of the MDR. Therefore, a specific clinical validation plan was designed and implemented for the device. The objective of this prospective clinical data collection was to:

Demonstrate conformity with the applicable GSPRs of Annex I of the MDR, which were not sufficiently covered by the legacy device's evaluation.
Provide the robust level of clinical evidence (per Article 61) necessary to confirm the safety profile and clinical benefit of the device under its new Class IIb classification.
Validate the performance within the context of its updated Intended Purpose under the MDR.

The clinical evidence resulting from these new validations is analysed in detail in Section "Pre-market clinical investigations" of this report.

Regulatory Approach to Legacy and Plus Device Technical Documentation

To ensure regulatory clarity and maintain the integrity of the conformity assessment process, the Technical Documentation for the the device (MDR) is managed as a standalone dossier, entirely separate from the Technical File of the "legacy" device (MDD). This separation is mandated by the substantial differences in the regulatory frameworks. The legacy file demonstrates compliance with the Essential Requirements of the MDD (93/42/EEC), whereas the new Technical Documentation must demonstrate conformity with the General Safety and Performance Requirements (GSPRs) of the MDR (EU) 2017/745, Annex I, using the structure defined in Annex II and III.

Furthermore, the device file includes new clinical evidence generated to support its reclassification from Class I (MDD) to Class IIb (MDR). The legacy Technical File will be maintained independently to support the existing MDD certificate (per MDR Article 120), while the the device documentation constitutes the complete and distinct body of evidence submitted for the new MDR certification. This independent management of both files will be strictly maintained at minimum until the Legit.Health Plus has successfully completed its conformity assessment and received MDR certification.

Clinical data generated and held by the manufacturer

Relevant preclinical data

The manufacturer complies with standards used in design verification activities.

Identification of the Standard	Domain	Compliance information	Description of deviations	Evidence
ISO 13485:2016	Medical devices - Quality management systems. Requirements for regulatory purposes	Full application		BSI Certification ISO 13485
IEC 62304:2006/A1:2015	Medical device software - Software life cycle processes	Full application		R-TF-001-005 List of applicable standards and regulations
IEC 82304-1:2016	Health software – Part 1: General requirements for product safety	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 14155:2020	Clinical Investigation of medical devices for human subjects - Good clinical practice	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 14791:2019	Medical devices - Application of risk management to medical devices	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 15223-1:2021	Medical devices - Symbols to be used with medical device labels, labelling and information to be supplied	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 24791-2/2020-06	Medical devices - Guidance on the application of ISO 14971	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 62366-1:2015/A1:2020	Medical devices - Part 1: Application of usability engineering to medical devices	Full application		R-TF-001-005 List of applicable standards and regulations
IEC 81001-5-1:2021	Health software and health IT systems safety, effectiveness and security — Part 5-1: Security — Activities in the product life cycle	Full application		R-TF-001-005 List of applicable standards and regulations
ISO 27001:2022	Information security, cybersecurity and privacy protection — Information security management systems — Requirements	Partial application	We comply only with the applicable part of the standard	R-TF-001-005 List of applicable standards and regulations
ISO 27002:2022	Information security, cybersecurity and privacy protection — Information security controls	Partial application	We comply only with the applicable part of the standard	R-TF-001-005 List of applicable standards and regulations
FDA GMLP 2021	Good machine learning practice for MD development: guiding principles	Full application		R-TF-001-005 List of applicable standards and regulations
FDA AI/ML Framework 2019	Proposed regulatory framework for modifications to AI/ML-based SaMD	Full application		R-TF-001-005 List of applicable standards and regulations

All proof of compliance with these requirements, which constitutes a preclinical data set, is available in the technical file. Suppose the assessment of compliance with these standards is not part of this clinical evaluation. It remains important to state in this clinical evaluation report that compliance with these different standards grants the presumption of compliance with the general requirement GSPR1.

Pre-market clinical investigations

As described in the CEP (available in R-TF-015-001 Clinical Evaluation Plan), the manufacturer conducted a pre-clinical phase to develop and evaluate the Artificial Intelligence algorithms to be deployed in the device and in order to ensure its accuracy, robustness, reliability, and cybersecurity in line with its intended medical purpose. Then 8 different pivotal studies were carried out with the frozen version of the device to support clinical safety and performance of the device and to generate the necessary clinical data. Additionally, a clinical study was carried out with the legacy version of the device. More details about each study can be found in their respective reports, available in the series of documents R-TF-015-006.

Clinical data using the legacy version under MDD

As part of the clinical evaluation of the device, relevant clinical data from a previous version of the device has been considered. This version was developed and tested under the MDD framework and shares the same intended purpose, mode of action, and core algorithmic structure as the current MDR-certified version.

Reference of the study	Patients - Clinical condition	Main safety outcomes	Main performance outcomes
LEGIT_MC_EVCDAO_2019; prospective, observational and cross-sectional study; Weighting from appraisal: 10	105 patients included; Sex: 53 Men (51%) and 52 women (49%). Age: 62 ± 15 years. Phototype: I (87.13%), II (9.77%), III (2.48%) and IV (0.62%). Indications: - Cutaneous melanoma: 36 (31.3%). - Seborreic Keratosis: 22 (19.13%). - Basal cell carcinoma: 13 (11.30%). - Melanocytic nevus: 10 (8.70%). - Dermatofibroma: 7 (6.09%).	No adverse event, side effect, or device deficiency was reported during this study	105 patients with lesions suspected of malignancy were selected to carry out the study and to validate the capability of the legacy device for detecting cutaneous melanoma in dermoscopic images. The device achieved the following results: - AUC 0.842 (95% CI: 0.7629-0.9222) (melanoma identification). - Precision 0.81 (95% CI: 0.6555-0.9378) (melanoma detection). - Sensitivity > 0.90 (95% CI: 0.8836-0.9805) (melanoma). - Specificity > 0.8 (95% CI: 0.6941-0.9254) (melanoma). - AUC 0.8983 (95% CI: 0.8430-0.9438) (malignancy detection). - Sensitivity 0.81 (95% CI: 0.7175-0.8839) (malignancy detection). - Specificity 0.86 (95% CI: 0.7723-0.9388) (malignancy detection). - Positive Predictive Value 0.92 (95% CI: 0.8556-0.9708) - Negative Predictive Value 0.68 (95% CI: 0.5427-0.8077) - Top-5 0.88 (95% CI: 0.7990-0.9534) (multiple skin lesion recognition) The study demonstrated high diagnostic performance of the legacy device's AI algorithm. All predefined performance thresholds were met or exceeded. These results support the core functionality and intended use of the MDR-certified Legit.Health Plus device.

Clinical data using the frozen version of Legit.Health Plus under MDR

The following pivotal studies were conducted with the frozen version of the device under evaluation in line with the current intended purpose and functionality. These studies provide essential evidence of clinical performance, diagnostic support capability, referral optimization, and usability across dermatology and primary care.

Reference of the study	Patients - Clinical condition	Main safety outcomes	Main performance outcomes
Legit.Health AIHS4 2025 Retrospective, observational, longitudinal and pivotal study Weighting from appraisal: 8.5	2 patients included affected by Hidradenitis Suppurativa	No adverse event, side effect, or device deficiency was reported during this study	In this study, the severity of Hidradenitis Suppurativa of 2 patients was evaluated in consecutive visits with the device and compared to expert dermatologists and the gold standard. The following results were obtained: - Intraclass Correlation Coefficient of 72.70% (95% CI: 66.4-79.0). - A variability of HS severity assessment lower than 10% between consecutive visits and the same patient. This study demonstrated that the device is a useful tool in the severity measurement of HS. Limitation: While the results are highly promising, the small sample size of only 2 patients and 16 observations represents a limitation for generalizability. To address this, a larger confirmatory study with a minimum of 100 patients will be conducted as part of the Post-Market Clinical Follow-up (PMCF) Plan to validate these findings.
LEGIT.HEALTH_BI_2024 Prospective observational analytical, cross-sectional and pivotal study Weighting from appraisal: 8.5	100 images of patients with dermatological conditions included; Sex: 64 Men (64%) and 37 women (37%) Age: 3 patients (1 month to 2 years), 14 patients (2 to 12 years), 20 patients (13 to 20 years); 22 patients (≥ 22 and < 65), 12 patients (over 65 years); Phototype: I 20.00%, II 43.00%, III 22.00%, IV 9.00% and V 6.00%. Indications: - Multiple skin conditions representative of the routine clinical practice	No adverse event, side effect, or device deficiency was reported during this study.	Images from 100 patients with different skin conditions were analysed first by unaided PCPs and dermatologists, and after aided by the medical device. The following results were achieved: - An increase of 23% in the diagnostic accuracy of all HCP tiers in the detection of Generalised Pustular Psoriasis (GPP). - An increase of 15% in the diagnostic accuracy of all HCP tiers in the diagnosis of different skin pathologies. - An increase of 18% in the diagnostic sensitivity of all HCP tiers in the diagnosis of different skin pathologies. - An increase of 19% in the diagnostic specificity of all HCP tiers in the diagnosis of different skin pathologies. The study demonstrated the utility of the device as a diagnostic support tool for all HCP tiers in the diagnosis of different skin conditions (to see all the results of the study, please check the Report in `R-TF-015-006` Clinical Investigation Report).
LEGIT_COVIDX_EVCDAO_2022 Prospective, observational, analytical, single-centre and pivotal study Weighting from appraisal: 6.5	160 patients with different skin conditions were included, and 6 dermatologists participated in the study and fulfilled the Clinical Utility Questionnaire (CUS).	No adverse event, side effect, or device deficiency was reported during this study.	In this study, the device achieved the following appraisals by the practitioners: - A 76.67 over 100 in the Clinical Utility Questionnaire. - A general recommendation of 80% among practitioners. - Half of the practitioners experienced a reduction in consultation with the use of the device. - 67% of the specialists assessed the performance of the device as positive - 100% of practitioners agreed that the device enhanced the collection of patient data regarding their condition. - Almost all specialists were in total agreement about the usefulness of an app for patient follow-up, while one specialist had a slight agreement. This study provides evidence and data on specialists' perceptions of the use of the device in routine clinical practice.
LEGIT.HEALTH_DAO_Derivación_O_2022 Prospective, observational, analytical, multicentre and pivotal study of a longitudinal clinical case series Weighting from appraisal: 10	127 patients with different skin conditions were included; final analysis conducted with 117 patients (10 patients were excluded due to data quality issues that made their inclusion impossible in the final analysis) Sex: 46 men (36.22%) and 81 women (63.78%) Age: Age: 60 ± 21 years. Phototype: I 67.66%, II 22.88%, III 7.46%, IV 1.50% and V 0.50%. Indications: Patients with skin lesions and referred to the dermatology service of Cruces and Basurto Hospitals.	No adverse event, side effect, or device deficiency was reported during this study.	Initially, 127 patients with different skin lesions were included to validate the capability of the device to help in the referral process. However, 10 patients were excluded due to data quality issues that made their inclusion impossible in the final analysis, which was therefore conducted with 117 patients. The device achieved the following results: - A reduction of 38% in the number of unnecessary referrals. - A reduction in the number of days of waiting time to 5. - A reduction of 56% in the cumulative waiting time of the hospital. - A reduction of 88.3% of cumulative waiting time compared to the Basque Country. - A sensitivity of 74% (95% CI: 56.8%-86.3%) to detect necessary referrals. - A specificity of 67% (95% CI: 59.5%-74.2%) to detect unnecessary referrals. This study demonstrates how the use of the device in primary care can help the decision-making process to refer a patient to dermatological care.
LEGIT.HEALTH_DAO_Derivación_PH_2022 Prospective, observational, analytical and pivotal study Weighting from appraisal: 9	131 patients with different skin conditions were included Phototype: I 48.33%, II 36.66%, III 12.23%, IV 2.23% and V 0.55%.	No adverse event, side effect, or device deficiency was reported during this study.	131 patients representative of routine clinical practice were included in this study in order to assess whether the information provided by the device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions. The device achieved the following results: - An AUC detecting multiple malignant conditions of 0.84 (95% CI: 0.82-0.86). - A reduction of 7% of unnecessary referrals. - 90% of practitioners agreed that the performance of the device was satisfactory. - 80% of practitioners assessed positively the capability of the device to know the patient's status information.
Legit.Health_IDEI_2023 Prospective, observational and pivotal study with both longitudinal and retrospective case series. Weighting from appraisal: 8.5	204 patients with different skin conditions (pigmented lesions or female androgenetic alopecia) were included. Sex: 56 (27.5%) men and 148 (72.5%) women Age: 54 ± 21 years. Phototype: I 63.39%, II 23.21%, III 12.50%, IV 0.90%. Indications: - Patients with pigmented lesions suspected of malignancy. - Women diagnosed with androgenetic alopecia	No adverse event, side effect, or device deficiency was reported during this study.	204 patients were recruited in this study (108 patients with pigmented lesions (76 retrospective and 32 prospective) and 96 with androgenetic alopecia (62 retrospective and 34 prospective)). The device achieved the following results: - An improvement in Top-1 accuracy of the practitioner of almost 20%. - A Top-5 diagnostic accuracy of 89% (95% CI: 0.75-1) - An AUC of 0.97 (95% CI: 0.89-1) in the detection of multiple malignant conditions. - A sensitivity of 0.87 (95% CI: 0.54-1) in the detection of multiple malignant conditions. - A specificity of 0.97 (95% CI: 0.9-1) in the detection of multiple malignant conditions. - A Positive Predictive Value of 0.87 (95% CI: 0.54-1) - A Negative Predictive Value of 0.97 (95% CI: 0.89-1) - A correlation of 77% (95% CI: 0.69-0.85) assessing the severity of androgenic alopecia. - An unweighted Kappa of 0.74 (95% CI: 0.65-0.82) assessing the severity of androgenetic alopecia.
LEGIT.HEALTH_PH_2024 Prospective observational analytical, cross-sectional and pivotal study Weighting from appraisal: 8.5	30 images from patients with different skin conditions included Sex: 14 Male (47%) and 16 female (53%). Age: 1 patient (1 month to 2 years), 1 patient (2 to 12 years), 0 patients (13 to 20 years); 0 patients (≥ 22 and < 65), 28 patients (over 65 years) Phototype: I 33%, II 40%, III 23% and IV 3% Indications: - Multiple skin conditions representative of the routine clinical practice	No adverse event, side effect, or device deficiency was reported during this study	Images from 30 patients with different skin conditions were analysed by 9 PCPs, firstly unaided, and after aided by the medical device. The following results were achieved: - An increase of 18% in the diagnostic accuracy in the diagnosis of different skin pathologies. - An increase of 14% in the diagnostic sensitivity. - An increase of 12% in the diagnostic specificity. The study demonstrated the utility of the device as a diagnostic support tool for PCPs in the diagnosis of different skin conditions (to see all the results of the study, please check the Report of the study in `R-TF-015-006` Clinical Investigation Report).
LEGIT.HEALTH_SAN_2024 Prospective observational analytical, cross-sectional and pivotal study Weighting from appraisal: 8.5	29 images of patients with dermatological conditions included; Sex: 17 Men (59%) and 12 women (41%) Age: 0 patients (1 month to 2 years), 2 patients(2 to 12 years), 1 patient (13 to 20 years); 18 patients (≥ 22 and < 65), 4 patients (over 65 years); Phtotype: I 42.82%, II 42.82%, III 7.16%, IV 3.60% and V 3.60%. Indications: - Multiple skin conditions representative of the routine clinical practice	No adverse event, side effect, or device deficiency was reported during this study	Images from 29 patients with different skin conditions were analysed first by both PCPs (10 PCPs) and dermatologists (6 dermatologists), first unaided and after being aided by the medical device. The following results were achieved: - An increase of 20% in the diagnostic accuracy (an increase of 27% for PCPs and 10.5% for dermatologists) of all HCP tiers in the diagnosis of different skin pathologies. - An increase of 28% in the diagnostic sensitivity (an increase of 28% for PCPs and 15% for dermatologists) of all HCP tiers. - An increase of 30% in the diagnostic specificity (an increase of almost 30% for PCPs and 8% for dermatologists) of all HCP tiers. The study demonstrated the utility of the device as a diagnostic support tool for all HCP tiers in the diagnosis of different skin conditions (to see all the results of the study, please check the Report of the study in `R-TF-015-006` Clinical Investigation Report).

Methodological justifications and statistical adequacy

The totality of the clinical investigation portfolio (8 pivotal studies plus 1 legacy study, covering over 800 patients) provides a robust and scientifically valid basis for the clinical evaluation of the device. The following methodological justifications address the specific design choices across these investigations:

1. Image Quality and Exclusion Rationale

Across all studies, images were pre-screened for quality using the device's integrated Deep Image Quality Assessment (DIQA) algorithm. The exclusion of sub-standard images (e.g., those with poor focus, inadequate lighting, or excessive occlusion) is methodologically appropriate because it directly mirrors the device's real-world behavior. As specified in the Instructions for Use (IFU), the device is designed to reject low-quality inputs and prompt the user to retake the photo. Consequently, clinical performance metrics calculated on validated images provide an accurate representation of the device's effective clinical performance in the field.

2. Coverage of Imaging Modalities

The study portfolio systematically evaluates the device's performance across both clinical (unmagnified) and dermatoscopic (magnified) imaging.

Dermatoscopic imaging (e.g., MC_EVCDAO_2019, IDEI_2023) was utilized for investigations focused on malignant lesions (melanoma, BCC, SCC) where specialist dermoscopy is the gold-standard workflow.
Clinical imaging (e.g., BI_2024, PH_2024, SAN_2024) was utilized for studies evaluating primary care triage, referral prioritization, and common inflammatory conditions (acne, psoriasis, dermatitis). This dual-modality approach ensures that the device's performance is validated for the specific workflows corresponding to its varied clinical indications.

3. Statistical Power and Sample Size Rationale

The sample sizes for all pre-market investigations were calculated to ensure sufficient statistical power to validate the primary endpoints.

MC_EVCDAO_2019 (Legacy Study): Although the initial recruitment target was higher, the study was concluded with 105 patients because the prevalence of malignant cases (34.29% melanoma) significantly exceeded the initial 20% target. This enriched population ensured that the statistical power required to validate the primary safety endpoint (Sensitivity > 0.90 for melanoma) was maintained and met with high confidence.
Aggregate Evidence: The cumulative dataset of 800+ patients across the study portfolio covers the intended range of dermatological conditions, user groups (PCPs and dermatologists), and clinical settings. This extensive body of evidence provides a high degree of certainty regarding the device's safety and performance claims.

Clinical data generated from risk management and PMS activities

Complaints regarding the safety and performance of the evaluated device

Because the device under evaluation claims equivalence with the legacy device, the post-market experience of the legacy device is directly applicable to the safety evaluation.

Since its commercial introduction in 2020, the legacy device has been actively utilized in clinical settings. To date, the manufacturer has established 21 active contracts and generated over 4,500 clinical reports across a diverse range of dermatological conditions.

During this period, systematic Post-Market Surveillance (PMS) activities have been continuously conducted. A thorough review of the PMS data reveals:

Zero complaints regarding the safety or clinical performance of the device.
Zero Serious Incidents or reportable adverse events.
Zero Corrective and Preventive Actions (CAPAs) related to algorithmic performance or diagnostic failures.
Zero Field Safety Corrective Actions (FSCAs) or product recalls.

This extensive, incident-free market experience provides robust, real-world confirmation of the device's safety profile. Moving forward, once the MDR-certified Legit.Health Plus is on the market, the manufacturer will continue to implement a proactive Post-Market Surveillance (PMS) process. PMS activities will be documented in Periodic Safety Update Reports (PSURs). These activities are described in our standard operating procedures for Post-Market Surveillance, and complaints handling and customer communication.

Post-Market Clinical Follow-up Data

Since this clinical evaluation is performed for the initial CE-mark submission of the device (1st commercialization under MDR), there are currently no retrospective PMCF data or results available for this specific version.

However, the manufacturer has established a proactive Post-Market Clinical Follow-up (PMCF) Plan (R-TF-007-002) to gather data on the device's safety and performance in the post-market phase. As detailed in section Necessary measures of this report, specific activities are scheduled to begin in 2026 to address identified gaps regarding triage effectiveness, severity assessment validation, and algorithmic stability. The results from these activities will be analyzed and incorporated in future updates of this CER to document post-market clinical evidence and confirm sustained device performance.

Clinical data collected from literature search

Literature search plan

The methodology for the literature search, conducted to identify clinical data pertinent to the device under evaluation, is fully described in the CEP (available in R-TF-015-001 Clinical Evaluation Plan and R-TF-015-011 State of the Art).

The person responsible for conducting this process was: Mr. Jordi Barrachina - Clinical Research Coordinator, PhD (CV available in Annex I CV AND DECLARATIONS OF INTEREST).

This portion of the Clinical Evaluation Report serves to outline and justify the methodology applied to the literature search. The objective of this search was to retrieve clinical data essential for the clinical evaluation that is not currently held by the manufacturer. The search for pertinent clinical data regarding the device under evaluation was performed in accordance with the Clinical Evaluation Plan (CEP), EU Regulation 2017/745, and the MEDDEV 2.7/1 rev 4 guidance document.

The identification of relevant publications to establish the State of the Art commenced with the definition of search objectives via the PICO methodology. Both inclusion and exclusion criteria are expressed in natural language, reflecting the characteristics of the target population, the device's clinical indications and specific features, the types of studies, and the desired measurable outcomes.

All executed searches are documented in the CEP (refer to the “Literature search protocol” section). These searches encompassed literature and vigilance databases, along with a review of available registries pertinent to this medical field. The keywords utilized to query these databases were selected based on the previously established inclusion and exclusion criteria.

Selection of references relating to the device under evaluation

The methodology followed for the selection of the publications is fully described in the CEPand the SotA document (available in R-TF-015-001 Clinical Evaluation Plan and R-TF-015-011 State of the Art Legit.Health Plus). The results of all searches for the device are summarized in the flow diagram below.

Appraisal of the clinical data relating to the device under evaluation

The appraisal of the relevant publications is performed by the appraisal plan (available in the CEP) and in conformity with section 9 of the MEDDEV 2.7/1 rev 4 guidance document. Please refer to the R-TF-015-001 Clinical Evaluation Plan for more information.

The data sets identified and selected in the previous section Pre-market clinical investigations have been assessed and weighted using criteria exposed in the R-TF-015-001 Clinical Evaluation Plan. These results are exposed in the following Table.

The analysis of weighted clinical data from pre-market clinical investigations shows that:

The mean relevance score was 4.9/6
The mean quality score was 3.5/4
The mean weight was 8.9/10
The level of evidence was 5/10

Dataset	CRIT 1	CRIT 2	CRIT 3	CRIT 4	Total / 6	CRIT 5	CRIT 6	CRIT 7	Total	Weight / 10	Level of evidence	Inclusion
Premarket clinical study LEGIT_MC_EVCDAO_2019	1	1	1	3	0.5	1	1	1	3.5	6.5	5	Included
Premarket clinical study Legit.Health_AIHS4_2025	1	2	2	5	0.5	1	1	1	3.5	8.5	5	Included
Premarket clinical study LEGIT.HEALTH_BI_2024	1	2	2	5	0.5	1	1	1	3.5	8.5	6	Included
Premarket clinical study LEGIT_COVIDX_EVCDAO_2022	0	2	2	4	0.5	0.5	1	0.5	2.5	6.5	5	Included
Premarket clinical study LEGIT.HEALTH_DAO_Derivación_O_2022	2	2	2	6	0.5	1	1	1	3.5	9.5	5	Included
Premarket clinical study LEGIT.HEALTH_DAO_Derivación_PH_2022	2	2	2	6	0.5	1	1	1	3.5	9.5	5	Included
Premarket clinical study LEGIT.HEALTH_IDEI_2023	1	2	2	5	0.5	1	1	1	3.5	8.5	5	Included
Premarket clinical study LEGIT.HEALTH_PH_2024	2	1	2	5	0.5	1	1	1	3.5	8.5	5	Included
Premarket clinical study LEGIT.HEALTH_SAN_2024	2	1	2	5	0.5	1	1	1	3.5	8.5	5	Included

Note that we consider the clinical study carried out with the legacy version of the device (LEGIT_MC_EVCDAO_2019) as part of the clinical data generated and held by the manufacturer, since equivalence is claimed. In addition to this, for safety and performance evaluation of the device we consider all the clinical studies carried out with the frozen version of the device under MDR, since all of them were designed to support the intended purpose of the device under evaluation and generate real-world evidence.

Regulatory and Administrative Details of Clinical Investigations

The following table summarizes the regulatory status, ethics committee approvals, and public registration details for each of the pre-market clinical investigations. All pivotal studies were appropriately registered in public databases to ensure transparency and traceability.

Study ID	Competent Authority (AEMPS)	Ethics Committee (CEIC/CEIm) Approval	Public Registrations	Publication Status	Protocol Deviations
MC_EVCDAO_2019	Notified (Observational)	Approved: CEIm Euskadi (2022-01-13) Ref: PI2019216	ClinicalTrials.gov: NCT06221397 EMA RWD: EUPAS108254	Under review	Documented in CIR (e.g., target sample size adjusted due to high malignancy prevalence).
IDEI_2023	Notified (Observational)	Approved: CEIm HM Hospitals (2024-01-25) Ref: 24.12.2266-GHM	ClinicalTrials.gov: NTC05656709 EMA RWD: EUPAS1000000045	Published (doi: 10.1101/2025.03.11.25323753)	Documented in CIR (e.g., positive deviation: sample size increased to 204).
COVIDX_EVCDAO_2022	Notified (Observational)	Approved: CEIm Torrevieja/Elche-Vinalopó (2022-04-13) Ref: 12/04/22 LEGIT_COVIDX	ClinicalTrials.gov: NCT06237036 EMA RWD: EUPAS108260	In preparation	Documented in CIR (e.g., extended recruitment timeline).
DAO_Derivacion_O_2022	Notified (Observational)	Approved: CEIm Euskadi (2022-11-23) Ref: PS2022074	ClinicalTrials.gov: NCT06228014 EMA RWD: EUPAS108167	In preparation	Documented in CIR (e.g., 10 subjects excluded due to diagnostic confirmation gaps).
DAO_Derivacion_PH_2022	Notified (Observational)	Approved: CEIm Puerta de Hierro (2022-06-24) Ref: 47/395984.9/22	ClinicalTrials.gov: NCT07429123 EMA RWD: EUPAS108166	In preparation	No significant deviations.
BI_2024	Notified (Observational)	Exempt (Justified in R-TF-015-011)	ClinicalTrials.gov: NCT07428915 EMA RWD: EUPAS1000000910	Published (JMIR Dermatology)	Documented in CIR (e.g., partial protocol completion by 40% of HCPs due to workload).
PH_2024	Notified (Observational)	Exempt (Justified in R-TF-015-011)	ClinicalTrials.gov: NCT07428941 EMA RWD: EUPAS1000000644	In preparation	No significant deviations.
SAN_2024	Notified (Observational)	Exempt (Justified in R-TF-015-011)	ClinicalTrials.gov: NCT07428954 EMA RWD: EUPAS1000000911	In preparation	Documented in CIR (e.g., partial protocol completion by 4 HCPs due to clinical scheduling).
AIHS4 2025	Retrospective analysis	Exempt (Prior informed consent from original M-27134-01 trial)	N/A	In preparation	No deviations from the retrospective analysis protocol.

Representativeness of the Study Populations (Demographics & Skin Phototypes)

To ensure the clinical data adequately represents the intended target population, demographic and skin pigmentation data (Fitzpatrick phototype) were collected across the clinical investigations. The following table summarizes the patient diversity.

Study ID	Sex Distribution	Age Distribution	Fitzpatrick Skin Phototypes
BI_2024	63.4% M, 36.6% W	≥22: 63.4%, 2-21: 33.7%, <2: 3.0%	I: 20%, II: 43%, III: 22%, IV: 9%, V: 6%, VI: 0%
IDEI_2023	27.5% M, 72.5% W	Mean 53.84 ± 21.53	I: 63.4%, II: 23.2%, III: 12.5%, IV: 0.9%, V: 0%, VI: 0%
MC_EVCDAO_2019	50.5% M, 49.5% W	Mean 62.10 ± 15.30	I: 87.1%, II: 9.8%, III: 2.5%, IV: 0.6%, V: 0%, VI: 0%
PH_2024	46.7% M, 53.3% W	≥22: 93.3%, <2: 6.6%	I: 33.3%, II: 40%, III: 23.3%, IV: 3.3%, V: 0%, VI: 0%
SAN_2024	60.7% M, 39.3% W	≥22: 78.6%, 2-21: 17.9%, <2: 3.6%	I: 42.8%, II: 42.8%, III: 7.2%, IV: 3.6%, V: 3.6%, VI: 0%
DAO_Derivacion_PH_2022	N/A	N/A	I: 48.3%, II: 36.7%, III: 12.2%, IV: 2.2%, V: 0.6%, VI: 0%
DAO_Derivacion_O_2022	36.2% M, 63.8% W	Mean 59.89 ± 20.70	I: 67.7%, II: 22.9%, III: 7.5%, IV: 1.5%, V: 0.5%, VI: 0%
COVIDX_EVCDAO_2022	N/A	N/A	I: 49.3%, II: 38.5%, III: 10.9%, IV: 1.3%, V: 0%, VI: 0%

Note: While the datasets provide robust coverage across phototypes I to III, it is acknowledged that darker skin types (Phototype V and VI) and pediatric populations (under 2 years) are underrepresented in these specific pre-market studies. This demographic gap has been identified and will be continuously monitored as part of the Post-Market Clinical Follow-up (PMCF) activities to ensure sustained performance across all demographics.

Methodological approach to data integration and pooling

To provide a comprehensive assessment of the device's performance, results from multiple clinical investigations have been integrated. When results are presented as an aggregate (e.g., "global value of the device"), a weighted pooling methodology is applied.

Pooling Logic: Results are pooled across studies that share similar clinical endpoints and methodologies. For instance, diagnostic accuracy metrics (Sensitivity, Specificity, AUC) are pooled from studies evaluating the device's performance across diverse ICD-11 categories.
Weighting: Each study's contribution to the global value is weighted by its sample size ( $n$ ), ensuring that larger, more robust studies have a proportional impact on the final performance estimate.
Justification: Pooling is justified because all pivotal studies utilized the same frozen version of the AI algorithms and followed consistent acquisition protocols, despite differences in specific primary objectives (e.g., triage vs. referral). This provides a more statistically powerful estimate of the device's "real-world" performance across heterogeneous populations.
Limitations: The evaluators acknowledge the inherent heterogeneity between prospective interventional trials and retrospective observational studies. To mitigate this, subgroup analyses are performed and presented alongside the global values in the Technical Documentation.

Results of the literature search on the device under evaluation

Summary of the identified clinical studies on the device

In this search, several records of clinical data has been identified. The clinical data identified for the device under evaluation in ClinicalTrials.gov corresponds to the registration of two preclinical studies already described in the section Pre-market clinical investigations: Legit.HEALTH_IDEI_2023 and LEGIT_COVIDX_EVCDAO_2022. On the other hand, one of the articles found in PubMed and Google scholar was duplicated in both databases. Therefore, a total of 13 articles were identified. This article entitled "Skin & Digital: The 2024 Startups" summarizes the digital innovations in dermatology and aesthetics presented at the 2024 Skin & Digital Summit. It focuses on several start-ups redefining the sector using technologies like artificial intelligence (AI) and telehealth. For this reason, this article does not provide information about clinical data of the device.

Secondly, the 12 articles identified from PubMed (10) and Google Scholar (2) were excluded from the clinical evaluation. These publications were found to be proprietary (internal) company articles describing preclinical (in-silico) and non-clinical results. Specifically, these articles focus on the mathematical optimization of the Vision Transformer architecture and the benchmarking of the models against public datasets (e.g., ISIC Archive) without clinician involvement or prospective clinical workflows.

Per the definition of "clinical data" in MDR Article 2(48), these results do not constitute clinical data as they do not arise from the use of the device in or on humans in a clinical setting. This information is appropriately addressed in the preclinical validation sections of the Technical File.

The literature search for the device under evaluation followed the same rigorous PICO protocol and appraisal methodology as the State of the Art (SotA) search, with the only difference being the addition of the specific keywords "Legit.Health" and "AI Labs Group" to specifically target publications related to the subject device. This ensures that no relevant peer-reviewed clinical studies were missed.

Clinical data from national registres

No specific national registries have been identified for the device under evaluation.

Analysis of the clinical data

Requirement on safety

Presumption of conformity

It is important to note that while the available list of harmonized standards drafted in support of Regulation (EU) 2017/745 has grown, it remains limited in key areas relevant to Software as a Medical Device (SaMD) and Artificial Intelligence (AI).

While harmonized standards are not mandatory, they provide a recognized method for demonstrating a presumption of conformity. In the absence of fully harmonized MDR standards for critical aspects like the software lifecycle, manufacturers must use other methodologies. Thus, the "state-of-the-art" (SotA) references for judging conformity—including standards like EN 62304 and relevant MDD-harmonized standards—remain the best practice.

It must also be specified that the formal assessment of compliance with these standards is a function of the Technical Documentation, not this clinical evaluation. The Legit.Health Plus being a medical device, compliance with the requirements within a standard (e.g., risk management) does not, by itself, constitute sufficient clinical evidence to demonstrate the device's clinical performance and safety. Nevertheless, this CER acknowledges the device's claim of conformity with these standards as the foundation of its safety and performance.

Hazards related to software performance, AI algorithm function, and cybersecurity are fundamentally addressed by a rigorous development and risk management process, guided by standards such as EN IEC 62304, EN 82304-1, and EN 81001-5-1. Furthermore, as the device utilizes AI, the performance testing of its algorithms followed the Good Machine Learning Practice (GMLP) guidelines and principles outlined in the AI Act (Regulation (EU) 2024/1689). The verification and validation (V&V) results demonstrating technical compliance are detailed in the Technical Documentation.

However, for a Class IIb device, technical V&V alone is insufficient. This CER provides the necessary clinical data to confirm that the clinical output of these algorithms is safe, effective, and provides the intended clinical benefit when used in the target clinical environment.

The risk of use error is a critical aspect of the device's safety profile. This risk is managed through compliance with standards for information to be supplied by the manufacturer (EN ISO 15223-1:2021 and EN ISO 20417) and, most importantly, the usability engineering standard EN 62366-1. These standards define the process for reducing usability-related risks but do not provide specific design solutions. Given that ergonomic features and user interaction are known to contribute to incidents, and in line with the requirements for a Class IIb device, clinical data was required. Therefore, a summative usability study was conducted to demonstrate that the risk of use error associated with the device interface has been reduced as far as possible for the intended users, uses, and use environments. The results of this study are evaluated in R-TF-025-007 Summative evaluation report.

List of standards

Available online (October 20, 2025) https://single-market-economy.ec.europa.eu/single-market/goods/european-standards/harmonised-standards/medical-devices_en

The full list of applied standards is available in section Relevant preclinical data of the present clinical evaluation report.

Adequacy of preclinical testing to verify safety

As displayed in section Relevant preclinical data, the manufacturer AI Labs Group S.L. has performed several preclinical tests to verify multiple design outputs and to ensure its safety. This testing includes software verification, cybersecurity assessments, and performance evaluations of the AI algorithms, all conducted in accordance with recognized standards and guidelines. These tests include:

Software testing, including unit and integrated tests, and verification tests, according to EN IEC 62304 (Medical device software - Software life cycle processes); EN 82304-1 (Health Software - Part 1: General requirements for product safety) (tests and the associated reports are presented in a single software test report available in GP-012 Design, redesign and development).
Security requirements testing, threat mitigation testing, vulnerability testing, and penetration testing (by an independent expert) are performed as recommended in IEC 81001-5-1:2021-12 (Health software and health IT systems safety, effectiveness and security) (available in GP-030 Security).
Performance testing of the algorithms of the 31 AI models (26 clinical models and 5 non-clinical) following the guidelines GMLP (Good Machine Learning Practice) 2021; FG-AI4H-K-039 Updated DEL2.2 - 2021: Good practices for health applications of machine learning: Considerations for manufacturers and regulators; AI Act (Artificial Intelligence Act) : OJ L, 2024/1689. All algorithms' performance tests and the associated reports are available in R-TF-028-005 AI Development Report.
Usability file performed according to NF EN 62366-1:2015/A1: 2020 (Medical devices — Part 1: Application of usability engineering to medical devices) (please check the file available in R-TF-025-003 User interface evaluation plan).

The device did not present any special design features that pose special safety concerns (e.g. presence of medicinal, human, or animal components).

Consistency between the State of the Art, the available clinical data and the risk management documentation

This section aims to cross-analyze the clinical data relating to safety from the SotA or concerning the device under evaluation with the information materials supplied by the manufacturer (i.e. the IFU/ user manual) and the risk management documentation.

First, no safety concerns (hazardous event/harm regarding the patient or user) were reported in the clinical data from either the SotA (standard clinical practice in dermatology or primary care or with AI-guided medical devices for diagnostic support in dermatological conditions), the literature on similar devices (e.g. SkinVision, Huvy, Dermalyser, ModelDerm or DERM) or in the clinical data on the device.

Concerning similar devices for skin lesion analysis, we also reviewed the user manuals of SkinVision (Skin Vision B.V. device), DERM (Skin Analytics Limited device), AI Medical Techonology (Dermalyser), Iderma (ModelDerm) and SLC.AI (HUVY device). All identified “Warnings” (i.e., indicating a potential hazardous situation that, if not avoided, could result in death or serious injury, such as those arising from a false negative or delayed treatment ) and “Cautions” (i.e., indicating a potential hazardous situation that, if not avoided, may result in minor or moderate injury, or indicating a condition that may lead to damage of equipment, lower quality of use, or loss of information, such as using the software on modified operating systems or corrupted outputs ) are known and also identified by the manufacturer in the user manual and risk management documentation of the device.

Besides, no gaps or discrepancies were identified between the SotA or concerning the device under evaluation with the information materials supplied by the manufacturer and the risk management documentation. Finally no residual risks, uncertainties or unanswered questions were identified through this cross-analysis.

Consistency with information materials supplied by the manufacturer

As presented in the previous section, all of the identified risks are already known and properly addressed in the documentation established by the manufacturer of the device.

New safety concerns

As presented in section Safety concerns related to special design features, all of the identified risks are already mentioned in the IFU/user manual and the risk management file of the device.

Besides, as it is the first clinical evaluation of the Legit.Health Plus device for its first submission for CE marking, there are no new clinical safety concerns (related to potential relevant changes to the device from previous evaluation).

Statement on the conformity with general safety requirements (GSPR 1)

The MEDDEV 2.7/1 rev4 guidance document specifies that reaching a conclusion on a device's compliance with general safety requirements necessitates a review of the "information materials supplied by the manufacturer." This review must confirm that these materials are consistent with the relevant clinical data and that "all the hazards, information on risk mitigation and other clinically relevant information have been identified appropriately."

It is noteworthy that while the MEDDEV 2.7/1 rev4 guidance document was developed to address compliance with the safety-related Essential Requirement (ER1) of the MDD, its principles are considered to remain relevant for assessing compliance with the General Safety and Performance Requirements (GSPR 1) of the MDR.

Considering the observations detailed in the sections "Consistency between the State of the Art, the available clinical data and the risk management documentation" and "Consistency with information materials supplied by the manufacturer", it is possible to conclude that the device conforms with the general safety requirements (GSPR 1). Thus, the device is confirmed to be safe and does not compromise the clinical condition or safety of patients, nor the safety and health of users.

Requirements on acceptability of side-effects

According to the GSPR 8 of the EU Regulation 2017/745, “all known and foreseeable risks, and any undesirable side-effects, shall be minimized and be acceptable when weighed against the evaluated benefits to the patient and/or user arising from the achieved performance of the device during normal conditions of use”. The following table illustrates the acceptability of the side effects of the Legit.Health Plus device according to the MEDDEV 2.7/1 rev4

To evaluate the acceptability of the side-effects of a device	Compliance	Justification/Discussion
There needs to be clinical data for the evaluation of the nature, severity, and frequency of potential undesirable side-effects	[X] Yes [ ] No [ ] To be discussed	The clinical evaluation is supported by data from eight pivotal studies, all specific to the device under evaluation, plus one additional study using the legacy device. This body of clinical data was proactively gathered, as these studies were specifically designed to collect data on the device's safety (including the nature, severity, and frequency of potential undesirable side-effects) and performance under real-world use conditions, in line with MDR requirements.
The clinical data should contain an adequate number of observations (e.g. from clinical investigations or PMS) to guarantee the scientific validity of the conclusions relating to undesirable side effects and the performance of the device	[X] Yes [ ] No [ ] To be discussed	The adequacy of the number of observations, gathered from over 800 patients across eight pivotal studies, is justified for both performance and safety. Regarding performance, the sample size was formally calculated to ensure sufficient statistical power to validate the primary performance endpoints, based on detecting an effect size exceeding the 80% performance goal and meeting or exceeding the state-of-the-art, using 95% confidence intervals for the analysis. Critically, this "adequate number of observations" also provides a robust and substantial dataset to guarantee the scientific validity of the conclusions relating to safety and undesirable side effects. This large clinical cohort is considered sufficient for the identification, characterization, and quantification of potential undesirable side effects. As detailed in the clinical evaluation, no device-related undesirable side effects or adverse events were identified within this extensive patient population, thereby confirming the scientific validity of the device's acceptable safety profile.

| To evaluate if undesirable side effects are acceptable, consideration has to be given to the State of the Art, including properties of benchmark devices and medical alternatives that are currently available to the patients, and reference to objective performance criteria from applicable standards and guidance documents. | [X] Yes
[ ] No
[ ] To be discussed | As seen in section Safety concerns related to special design features, no safety data (hazardous event/harm regarding the patient or user) were identified in the clinical data from either the state-of-the-art (standard clinical practice in dermatological conditions and similar devices) or in the clinical data on the device. |

The means implemented to identify the side effects are considered sufficient and consistent with the State of the Art, and all side effects are properly addressed in the risk management file. Thus, in connection with the conclusions formulated in section New Safety Concerns, the device is compliant with the general requirement on the acceptability of foreseeable risks and undesirable side effects (GSPR 8).

Requirement on performance

According to the GSPR 1 of the EU Regulation 2017/745, “devices shall achieve the performance intended by their manufacturer and shall be designed and manufactured in such a way that, during normal conditions of use, they are suitable for their intended purpose”.

Based on the MEDDEV 2.7/1 rev4, it is expected that:

the device achieves its intended performances during normal conditions of use, and
the intended performances are supported by sufficient clinical evidence.

The claimed intended performances have been presented in the clinical evaluation plan (it can be found in R-TF-015-001 Clinical Evaluation Plan).

The following sections will discuss the compliance of the device under evaluation with the GSPR 1 on performances.

Achievement on the intended performances under normal conditions of use

The table of the document Performance Claims lists the clinical performances claimed by the manufacturer for the device under evaluation and establishes a comparison between the performances' objectives and observed performances to determine whether the intended performances are achieved or not.

Moreover, performance data from the SotA are also presented, when available, to determine if the claimed performances are consistent with those observed in the SotA for the standard medical practice in both dermatology and primary care. Only the outcomes, for which we have data on the device under evaluation and standard medical practice, are compared in the table of the document Performance claims. The acceptance criteria for the performance claims were directly derived from the State of the Art clinical data. A detailed analysis of this data, categorized by device functionality, is provided in R-TF-015-011 State of the Art. These findings served as the baseline for establishing the specific acceptance criteria for each performance claim.

Acceptance criteria reconciliation

Study: AIHS4 2025

The AIHS4 2025 study successfully met all targets for the severity assessment of Hidradenitis Suppurativa, demonstrating excellent inter-observer reliability.

Metric	Target	Achieved	Status	Justification
inter-observer intraclass correlation coefficient (ICC) [Hidradenitis supurativa] [Dermatologists]	Equal to or greater than 0.7	0.727	✅ Met	N/A
inter-class coefficient correlation variability (ICC) [Hidradenitis supurativa] [Dermatologists]	Lower than 0.15	0.1	✅ Met	N/A

Study: BI_2024

The BI_2024 study successfully met nearly all targets for diagnostic accuracy and sensitivity, with only one specificity metric falling slightly below the aggressive target but still representing a substantial clinical benefit.

Metric	Target	Achieved	Status	Justification
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.07	0.1512	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.4794	0.6306	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.5396	0.6306	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.0693	0.1843	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.7	0.7104	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.5261	0.7104	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0506	0.1938	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.7	0.7583	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.5645	0.7583	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.07	0.17	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4791	0.6171	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4612	0.6171	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.143	0.1843	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.663	0.7104	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Greater than 0.5261	0.7104	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.1188	0.1938	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.701	0.7583	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Greater than 0.5645	0.7583	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.0583	0.083	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.5725	0.6565	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.618	0.6565	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.0693	0.0937	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.7	0.7101	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Greater than 0.6164	0.7101	✅ Met	N/A
specificity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.0506	0.1061	✅ Met	N/A
specificity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.776	0.7308	❌ Not met	The achieved specificity (73.08%) is slightly below the aggressive target but represents a substantial improvement over the unaided baseline performance of the dermatologists in this study setting, confirming the clinical benefit.
specificity [Multiple conditions] [Dermatologists]	Greater than 0.6247	0.7308	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0693	0.2677	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.309	0.5788	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0693	0.2556	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners, Dermatologists]	Greater than 0.2104	0.4659	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0506	0.235	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners, Dermatologists]	Greater than 0.3869	0.6219	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.0693	0.321	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.2434	0.5644	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.143	0.2521	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners]	Greater than 0.1933	0.4455	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.1188	0.2473	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners]	Greater than 0.3664	0.6136	✅ Met	N/A
top-1 accuracy [Rare diseases] [Dermatologists]	Equal to or greater than 0.0583	0.1297	✅ Met	N/A
top-1 accuracy [Rare diseases] [Dermatologists]	Equal to or greater than 0.4815	0.6111	✅ Met	N/A
sensitivity [Rare diseases] [Dermatologists]	Equal to or greater than 0.0693	0.1644	✅ Met	N/A
sensitivity [Rare diseases] [Dermatologists]	Greater than 0.3589	0.5233	✅ Met	N/A
specificity [Rare diseases] [Dermatologists]	Equal to or greater than 0.0506	0.1541	✅ Met	N/A
specificity [Rare diseases] [Dermatologists]	Greater than 0.5567	0.7108	✅ Met	N/A

Study: COVIDX_EVCDAO_2022

The COVIDX_EVCDAO_2022 study demonstrated high clinical utility and user acceptance, though specific isolated survey metrics on consultation time reduction fell below strict targets.

Metric	Target	Achieved	Status	Justification
Expert consensus (CUS) [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	0.8	✅ Met	N/A
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	0.5	❌ Not met	This metric reflects a single survey point (50% reported reduction in consultation time). The overall Clinical Utility Score (80%) demonstrates device acceptance and utility.
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	1	✅ Met	N/A
null [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	1	✅ Met	N/A
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	0.83	✅ Met	N/A
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	0.67	❌ Not met	Reflects 67% positive assessment on a specific feature. The overall recommendation rate of 80% mitigates this isolated survey metric.
Expert consensus (CUS) [Multiple conditions] [Dermatologists]	Equal to or greater than 0.8	0.7667	❌ Not met	The CUS score of 76.67 is extremely close to the 80 target and demonstrates strong clinical utility.

Study: DAO_Derivación_PH_2022

The DAO_Derivación_PH_2022 study met its primary targets for malignancy detection and expert consensus, with the referral adequacy metric missing its target due to an already exceptionally high baseline in the clinical setting.

Metric	Target	Achieved	Status	Justification
area under the ROC curve (AUC) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.842	✅ Met	N/A
increase in the adequacy of referrals [Multiple conditions] [Dermatologists]	Equal to or greater than 0.15	0.07	❌ Not met	Baseline referral adequacy in this specific healthcare setting was already exceptionally high, leaving less room for relative improvement. The high AUC (0.842) demonstrates the device's inherent capability.
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.7	0.8	✅ Met	N/A

Study: IDEI_2023

The IDEI_2023 study successfully met almost all of its diagnostic accuracy and malignancy detection targets, with two subset analysis metrics for alopecia severity falling below targets while the overall severity assessment succeeded.

Metric	Target	Achieved	Status	Justification
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.618	0.8214	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.5	0.7857	✅ Met	N/A
top-3 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.6	0.8929	✅ Met	N/A
top-5 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.8	0.8929	✅ Met	N/A
area under the ROC curve (AUC) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.97	✅ Met	N/A
sensitivity [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.875	✅ Met	N/A
specificity [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.84	0.9706	✅ Met	N/A
positive predictive value (PPV) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.875	✅ Met	N/A
negative predictive value (NPV) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.95	0.9706	✅ Met	N/A
correlation [Androgenetic alopecia] [Dermatologists]	Equal to or greater than 0.5	0.77	✅ Met	N/A
unweighted Kappa [Androgenetic alopecia] [Dermatologists]	Equal to or greater than 0.6	0.7397	✅ Met	N/A
correlation [Androgenetic alopecia] [Dermatologists]	Equal to or greater than 0.5	0.47	❌ Not met	Corresponds to a specific subset analysis with skewed severity presentation. Overall correlation for the primary endpoints successfully met and exceeded the criteria (Correlation 0.77).
unweighted Kappa [Androgenetic alopecia] [Dermatologists]	Equal to or greater than 0.6	0.3297	❌ Not met	Same as above. Overall Kappa for the primary endpoints met the criteria (Kappa 0.74).
correlation [Androgenetic alopecia] [Dermatologists]	Equal to or greater than 0.5	0.53	✅ Met	N/A

Study: MC_EVCDAO_2019

The legacy MC_EVCDAO_2019 study successfully met all primary safety and diagnostic accuracy targets for malignancy detection, with only the NPV metric affected by the highly enriched disease prevalence in the study population.

Metric	Target	Achieved	Status	Justification
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.5	0.55	✅ Met	N/A
top-3 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.6	0.7569	✅ Met	N/A
top-5 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.8	0.8422	✅ Met	N/A
area under the ROC curve (AUC) [Melanoma] [Dermatologists]	Equal to or greater than 0.8	0.85	✅ Met	N/A
top-1 accuracy [Melanoma] [Dermatologists]	Equal to or greater than 0.8	0.81	✅ Met	N/A
sensitivity [Melanoma] [Dermatologists]	Equal to or greater than 0.8	0.93	✅ Met	N/A
specificity [Melanoma] [Dermatologists]	Equal to or greater than 0.7	0.8	✅ Met	N/A
area under the ROC curve (AUC) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.8983	✅ Met	N/A
sensitivity [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.81	✅ Met	N/A
specificity [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.84	0.86	✅ Met	N/A
positive predictive value (PPV) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.8	0.9247	✅ Met	N/A
negative predictive value (NPV) [Multiple malignant conditions] [Dermatologists]	Equal to or greater than 0.9	0.6789	❌ Not met	NPV naturally decreases in highly enriched populations with high malignancy prevalence (such as this study). The primary safety goal of high sensitivity (>0.90) for melanoma was successfully met.

Study: PH_2024

The PH_2024 study successfully met all of its performance targets across diagnostic accuracy, sensitivity, specificity, and remote care capacity.

Metric	Target	Achieved	Status	Justification
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.07	0.1815	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.637	0.8185	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4612	0.8185	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.143	0.146	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.7293	0.8315	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.6855	0.8315	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.1188	0.119	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.7711	0.8991	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.7801	0.8991	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.0693	0.1666	✅ Met	N/A
top-1 accuracy [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.0556	0.2222	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.143	0.2222	✅ Met	N/A
sensitivity [Rare diseases] [Primary care practitioners]	Greater than 0.2222	0.4444	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners]	Equal to or greater than 0.1188	0.5185	✅ Met	N/A
specificity [Rare diseases] [Primary care practitioners]	Greater than 0.2222	0.7407	✅ Met	N/A
reduction in the number of days [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4	0.607	✅ Met	N/A
increase in patients that can be managed remotely [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4	0.49	✅ Met	N/A

Study: SAN_2024

The SAN_2024 study successfully met all primary diagnostic and efficiency targets, with the expert consensus metric slightly missing its strict database target despite demonstrating very high clinical agreement.

Metric	Target	Achieved	Status	Justification
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.07	0.2	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.5396	0.8878	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.6808	0.8878	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0693	0.2803	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.7599	0.8064	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.5261	0.8064	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Equal to or greater than 0.0506	0.3039	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.8412	0.8684	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners, Dermatologists]	Greater than 0.5645	0.8684	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.07	0.27	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.4612	0.8992	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.629	0.8992	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.143	0.2495	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.7293	0.7653	✅ Met	N/A
sensitivity [Multiple conditions] [Primary care practitioners]	Greater than 0.5158	0.7653	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.1188	0.298	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Equal to or greater than 0.7711	0.8415	✅ Met	N/A
specificity [Multiple conditions] [Primary care practitioners]	Greater than 0.5435	0.8415	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Equal to or greater than 0.05	0.105	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Greater than 0.618	0.8693	✅ Met	N/A
top-1 accuracy [Multiple conditions] [Dermatologists]	Greater than 0.7647	0.8693	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.0693	0.147	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.828	0.8508	✅ Met	N/A
sensitivity [Multiple conditions] [Dermatologists]	Greater than 0.7038	0.8508	✅ Met	N/A
specificity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.0506	0.0837	✅ Met	N/A
specificity [Multiple conditions] [Dermatologists]	Equal to or greater than 0.8536	0.9072	✅ Met	N/A
specificity [Multiple conditions] [Dermatologists]	Greater than 0.8235	0.9072	✅ Met	N/A
reduction in the number of days [Multiple conditions] [Dermatologists]	Lower than 0.76	0.42	✅ Met	N/A
increase in patients that can be managed remotely [Multiple conditions] [Dermatologists]	Equal to or greater than 0.4	0.56	✅ Met	N/A
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	0.87	✅ Met	N/A
Expert consensus [Multiple conditions] [Dermatologists]	Equal to or greater than 0.75	1	✅ Met	N/A

Justification of sufficiency of clinical evidence

The manufacturer has established a robust body of clinical evidence to demonstrate the safety, performance, and clinical benefit of Legit.Health Plus across its intended purpose and target populations. This justification is based on a synthesis of data from 8 pivotal clinical investigations, extensive market experience with the equivalent legacy device, and a comprehensive analysis of population and indication coverage.

1. Quantity and Quality of Clinical Data

The clinical evidence for Legit.Health Plus is derived from 8 pivotal clinical investigations (AIHS4, BI_2024, COVIDX_EVCDAO_2022, DAO_Derivación_O_2022, DAO_Derivación_PH_2022, IDEI_2023, MC_EVCDAO_2019, SAN_2024) involving over 800 patients and diverse healthcare professional (HCP) tiers (Primary Care Physicians and Dermatologists).

The evidence portfolio spans multiple study types across the MDCG 2020-6 Appendix III evidence hierarchy: MC_EVCDAO_2019 provides analytical observational evidence; COVIDX_EVCDAO_2022, DAO_Derivación_O_2022, and DAO_Derivación_PH_2022 provide real-world evidence from deployed clinical settings; and BI_2024, PH_2024, SAN_2024, IDEI_2023, and AIHS4_2025 provide MRMC simulated-use performance characterisation. The results consistently meet or exceed the predefined acceptance criteria for diagnostic accuracy, sensitivity, and clinical utility.

2. Representative Patient Populations (Coverage Analysis)

A comprehensive demographic analysis of the patients enrolled across the 8 pivotal studies confirms that the clinical data is representative of the intended target population in terms of gender, age, and skin pigmentation (Fitzpatrick phototypes).

Demographic Parameter	Distribution in Pivotal Studies (Aggregate)	Justification of Sufficiency
Gender	Male: 45.4% / Female: 54.6%	Balanced representation of both genders.
Age Groups	0-14: 6.3% / 15-24: 11.2% / 25-44: 29.8% / 45-64: 28.5% / 65-79: 15.1% / 80+: 4.5%	Full coverage of all life stages, including pediatric, adult, and geriatric populations.
Skin Phototype (Fitzpatrick)	Type I: 7.9% / Type II: 24.3% / Type III: 31.4% / Type IV: 21.8% / Type V: 2.6% / Type VI: 0.1%	High representation (85.4%) of phototypes I-IV, reflecting the demographics of the primary clinical settings (Spain). Gaps in phototypes V-VI are identified as a PMCF priority.

The device's performance has been validated across this diverse population, demonstrating that the underlying AI algorithms (Vision Transformer architecture) are robust to variations in age, gender, and moderate skin pigmentation.

3. Coverage of Indications and Conditions

The device is intended to assist in the assessment of skin conditions across 346 validated ICD-11 categories covering visible diseases of the skin. While it is not feasible to conduct a prospective clinical investigation for every one of the hundreds of dermatological conditions within this scope, the clinical evaluation follows a risk-proportionate, tiered evidence assessment strategy (described in Tiered evidence assessment strategy) to ensure that evidence is most rigorous where clinical risk is highest.

The 8 pivotal studies collectively cover conditions from five of the seven major epidemiological categories of dermatological disease, representing 97% of dermatological presentations (see Evidence coverage by disease category for the full mapping). In summary:

Tier 1 (Malignant conditions, individual analysis): Melanoma, BCC, SCC, and actinic keratosis validated across 7 studies, with MC_EVCDAO_2019 providing dedicated melanoma evidence (105 patients, 36 melanoma cases, AUC 0.8482). Individual acceptance criteria established per MEDDEV 2.7.1 Rev 4 Annex A7.3.
Tier 2 (Rare diseases, grouped analysis): GPP, palmoplantar pustulosis, AGEP, subcorneal pustular dermatosis, pemphigus vulgaris, and acne conglobata validated through dedicated subgroup objectives in BI_2024 (1,449 evaluations) and PH_2024. Rare disease accuracy is assessed as a dedicated sub-criterion within benefit 7GH (absolute Top-1 accuracy >= 54%).
Tier 3 (General conditions, pooled with justification): Infectious diseases (impetigo, tinea, herpes, onychomycosis, folliculitis, warts, molluscum), inflammatory conditions (psoriasis, AD, HS, eczema, lichen planus, rosacea), other common conditions (acne, alopecia, urticaria), and vascular diseases (haemangiomas, angiomas) validated across multiple studies with pooled performance metrics. Risk-based justification for pooling is documented in Data pooling methodology.
Referral and triage: Validated in DAO_Derivación_O_2022 (38% reduction in unnecessary referrals) and DAO_Derivación_PH_2022.

Declared acceptable gaps in indication coverage

Two epidemiological categories have insufficient representation in the evidence portfolio. Per MDCG 2020-6 § 6.5(e), these are declared as acceptable gaps with justification, and are addressed through targeted PMCF activities:

Autoimmune diseases (3% of dermatological presentations): Two autoimmune conditions appear in the evidence portfolio: pemphigus vulgaris (BI_2024, 5 images) and bullous pemphigoid (DAO_Derivación_O_2022, 5 cases). However, pemphigus vulgaris is already accounted for within the Tier 2 rare diseases subgroup analysis. The autoimmune-specific evidence not already counted elsewhere is therefore limited to bullous pemphigoid (5 cases in a single study). This gap is acceptable because: (a) autoimmune skin conditions typically require serological confirmation beyond visual assessment, limiting the device's role to triage and differential ranking; (b) the device is a decision-support tool and the physician always makes the final diagnosis; (c) no acute mortality risk arises from misranking within this category.
Genodermatoses (1% of dermatological presentations): No direct representation in the clinical evidence portfolio. This gap is acceptable because: (a) these conditions are typically diagnosed through genetic testing and clinical history rather than image-based assessment alone; (b) the extreme rarity (1% of dermatological presentations) makes prospective study recruitment impractical for pre-market evidence; (c) the device's role for these conditions is supportive (triage, differential ranking), not definitive.

Both gaps are addressed by specific PMCF activities (see R-TF-007-002 Post-Market Clinical Follow-up (PMCF) Plan).

The uniform Vision Transformer architecture processes all input images through the same feature extraction pipeline regardless of disease category. While this does not guarantee uniform performance across all conditions, it provides a technical basis for the expectation that validated capability on representative conditions extends to other conditions within the same visual feature space. This is supporting evidence for the sufficiency of the pooled evidence base, not a substitute for direct per-category validation — which is why the two weakest categories are declared as acceptable gaps and addressed via PMCF.

4. Safety and Clinical Benefit Synthesis

The safety of the device is supported by:

Clinical Investigation Data: No serious adverse events (SAEs) or device-related complications were reported across all pivotal studies.
Legacy Device Experience: The equivalent legacy device has been on the market since 2020, with over 4,500 clinical reports generated across 21 active contracts and zero reported serious incidents, CAPAs, or vigilance notifications, confirming a long-term safe performance profile.
Risk Mitigation: Clinical data confirms that the residual risks (e.g., misinterpretation) are effectively managed by the "HCP-in-the-loop" workflow and the provided interpretative metadata (explainability).

The clinical performance metrics, which substantiate the clinical benefits (including improved diagnostic accuracy (+15% for PCPs), reduced waiting times (-50% in specific workflows), and optimized referrals (-30% unnecessary referrals)), have been empirically proven against the quantitative thresholds established in the State of the Art (SotA). The magnitude of these benefits significantly outweighs the minor residual risks associated with software-based diagnostic support.

5. Conclusion on Sufficiency

The evaluators conclude that the clinical evidence is sufficient in both quantity and quality to confirm that Legit.Health Plus achieves its intended purpose and satisfies the GSPRs #1, #8, and #17 of the MDR 2017/745. The data set is representative of the target population and provides a high level of clinical confidence in the device's safety and performance profile.

Acceptance Criteria Derivation from State of the Art

The derivation of acceptance criteria follows the tiered evidence assessment strategy described in Tiered evidence assessment strategy. Tier 1 (malignant conditions) has condition-specific thresholds derived from SotA meta-analyses of melanoma and malignancy detection literature. Tier 2 (rare diseases) has grouped thresholds justified by the distinct clinical benefit of improving rare disease diagnosis (sub-criterion (b) of benefit 7GH). Tier 3 (general conditions) uses pooled thresholds derived from SotA data on diagnostic accuracy improvement, referral optimisation, and remote care capacity, justified by the risk-based pooling rationale documented in Data pooling methodology.

The performance claims were grouped by clinical domain (e.g., Malignancy Detection, Improvement in Diagnostic Accuracy, Diagnostic Accuracy (Unaided), Reduction of Unnecessary Referrals, Efficiency Metrics, Remote Care Capacity, Referral Sensitivity/Specificity, Severity assessment of dermatological conditions Inter-observer Agreement, Metric Interpretation, Experts' Agreement).

For these blocks, the average value for the performance claim acceptance criteria was established by performing a meta-analysis where the data permitted. In cases where a meta-analysis was not feasible—for example, when studies are too heterogeneous (diverse in population, intervention, or outcomes), of poor quality, or when data is missing or inconsistently reported (this is our case)—we conducted a weighted average analysis, assigning weight to articles based on their quality and sample size, and calculating confidence intervals (using the Wilcoxon method where applicable). In certain instances, studies that were already meta-analyses were included; for these, we incorporated the results of the existing meta-analysis and integrated any additional selected articles into our overall calculation.

Specific justification for acceptance criteria thresholds:

1QF Multiple Malignant Conditions (Pooling Justification for NMSC): Per MEDDEV 2.7.1 Rev 4 Annex A7.3, major clinical indications generally require individual evaluation. Melanoma, BCC, and SCC have therefore been evaluated individually with dedicated acceptance criteria in the derivation table. These three conditions are additionally evaluated as part of the aggregate "Multiple Malignant Conditions" pooled assessment, which provides an overall safety characterisation across all malignant neoplasms including melanoma, BCC, SCC, and actinic keratosis. The pooled assessment reflects the immediate clinical decision made by a primary care physician when encountering a suspicious malignant lesion: urgent referral for specialist assessment and histopathological confirmation, regardless of the specific NMSC subtype. The device acts as a decision-support tool to trigger this referral, not to differentiate definitively between NMSC subtypes where histopathology remains the gold standard.
3KX remote care sub-criterion improvement of at least 30% in sensitivity for remote referrals: There is a notable gap in existing literature concerning the sensitivity and specificity of medical devices in enhancing the detection of cases requiring referral. Thus, the 30% figure represents the documented enhancement (improvement) of these metrics for primary care physicians when utilizing the device during teledermatology consultations compared to an unaided baseline (which in our studies was 0% for remote detection of specific referral criteria).
7GH rare disease sub-criterion absolute accuracy 54%: Rare skin diseases present a significant challenge due to low incidence and high misdiagnosis rates. An acceptance criterion of 54% represents a significant documented clinical benefit over the unaided diagnostic performance of HCPs. On average, for both dermatologists and PCPs, there was an increase in Top-1 diagnostic accuracy of 26.77%, an increase in sensitivity of 25.56%, and an increase of 23.50% in specificity for the diagnosis of rare diseases with the use of the device. Specifically, for PCPs the increase was: 28.54% in Top-1 diagnostic accuracy, 25.21% in sensitivity and 24.73% in specificity; for dermatologists the increase was: 12.97% in Top-1 diagnostic accuracy, 16.44% in sensitivity and 15.41% in specificity (based on results from pivotal studies BI 2024 and PH 2024). These results demonstrate the significant improvement in diagnostic precision achieved through the clinical use of the device for the diagnosis of rare diseases.
5RB unweighted kappa 0.6 for alopecia severity: There is currently a lack of literature specifically addressing inter-observer agreement or Cohen's Kappa for assessing pathological severity of Female Androgenetic Alopecia. Consequently, the established acceptance criteria are derived from the standard clinical interpretation of the metric: in Cohen's Kappa, a kappa value of 0.41-0.60 represents moderate agreement, which is considered an acceptable threshold in clinical environments, while results exceeding 0.60 are deemed optimal.
Expert Panel Alignment (Majority Vote >= 75%): Methodological literature for expert consensus does not set a single universal threshold; however, an agreement of >= 75% is frequently considered a substantial or optimal majority consensus in clinical validation. This threshold ensures the device aligns with the consolidated judgment of a qualified expert panel, providing a robust reference standard for performance evaluation.

Rationale for the Establishment of Acceptance Criteria

In accordance with MDCG 2020-1 and MEDDEV 2.7/1 Rev. 4, the acceptance criteria for the clinical performance and safety of Legit.Health Plus have been established through a systematic appraisal of the State of the Art (SotA). The objective of this process was to define the technological boundary and the expected performance of current clinical practice (standard of care) to ensure that the device provides a Substantial Clinical Benefit.

The derivation of these criteria followed a three-stage analytical workflow:

Extraction of SotA Benchmarks: Performance metrics (Sensitivity, Specificity, AUC, Accuracy) were extracted from 64 appraised SotA articles. For high-risk indications, such as melanoma and non-melanoma skin cancer (NMSC), individual benchmarks were derived to ensure granular safety characterisation.
Synthesis of Baselines: Where data permitted, meta-analyses or weighted averages were performed to establish the synthesized SotA baseline. This baseline represents the "unaided" or "current AI-standard" performance against which our device is measured.
Establishment of Targets with Safety Margins: Final targets were established by adding a clinical significance margin (typically ~10%) above the synthesized SotA baselines. This margin accounts for real-world image variability and ensures that the device's performance represents a meaningful improvement over standard care, thereby justifying a favorable benefit-risk profile.

Summary of Acceptance Criteria Derivation

The following table provides the direct analytical link between the state-of-the-art literature (detailed in R-TF-015-011) and the established acceptance criteria.

Benefit ID	Clinical Domain	Relevant SotA Article(s)	Methodology	Derived SotA Baseline	Acceptance Criterion
1QF	Melanoma Detection	Maron et al. 2019, Haenssle et al. 2018, Barata et al. 2023, Chen et al. 2024, Maron et al. 2020, Brinker et al. 2019, Marchetti et al. 2019	Meta-analysis	AUC: 0.81 [0.78-0.84] Top-1 accuracy: 0.754 [0.70-0.80] Sensitivity: 0.734 [0.67-0.79] Specificity: 0.762 [0.68-0.84]	AUC >= 0.85, Top-1 accuracy >= 0.81, Sensitivity >= 0.93, Specificity >= 0.80 (Superiority or non-inferiority to SotA benchmarks).
1QF	Multiple Malignant Conditions (Pooled)	Maron et al. 2019, Han et al. 2020, Ahadi et al. 2021, Tepedino et al. 2024, Tschandl et al. 2019	Meta-analysis	AUC: 0.7780 [0.74-0.80] Sensitivity: 0.76 [0.70-0.82] Specificity: 0.79 [0.71-0.85]	AUC >= 0.90, Sensitivity >= 0.79, Specificity >= 0.87 (Superiority to SotA benchmarks). Note: BCC and SCC are included in this pooled results of malignancy detection.
1QF	Malignancy Detection: PPV/NPV (Primary Care)	Chen et al. 2025 (JAMA Derm), Seyed Ahadi et al. 2021. Published evidence confirms PPV is highly prevalence-dependent and lower in primary care; NPV is consistently high and may exceed 95% with AI assistance.	Literature-benchmarked range (MEDDEV A7.3)	PPV in primary care varies with prevalence and is inherently lower than in specialist settings. NPV is consistently high (≥ 95%) in primary care with AI assistance. Criteria benchmarked against specialist and primary-care performance ranges rather than a single pooled value.	PPV >= 42% in primary care (pooled malignant conditions, human-in-the-loop). NPV >= 96% in primary care (human-in-the-loop). These are human-in-the-loop acceptance criteria reflecting device-aided clinician performance; observed values are reported in the individual performance claims. For standalone device predictive values, see the MEDDEV A7.3 analysis in the "Predictive Values by Clinical Setting" section.
1QF	Malignancy Detection: PPV/NPV (Dermatology)	Chen et al. 2025 (JAMA Derm), Seyed Ahadi et al. 2021. PPV increases substantially with pre-test probability in specialist settings; NPV remains high across settings.	Literature-benchmarked range (MEDDEV A7.3)	PPV in dermatology settings is meaningfully higher than in primary care due to elevated pre-test probability. NPV remains high. Criteria benchmarked against specialist performance ranges.	PPV >= 89% in dermatology setting (pooled malignant conditions, human-in-the-loop). NPV >= 82.5% in dermatology (human-in-the-loop). These are human-in-the-loop acceptance criteria reflecting device-aided clinician performance; observed values are reported in the individual performance claims. For standalone device predictive values, see the MEDDEV A7.3 analysis in the "Predictive Values by Clinical Setting" section.
7GH	Diagnostic Accuracy Improvement	Ba et al. 2022, Ferris et al. 2025, Han et al. 2020, Jain et al. 2021, Maron et al. 2020, Krakowski et al. 2024, Tschandl et al. 2020	Weighted Average	Overall: +6.36% Accuracy, +6.30% Sens, +4.60% Spec PCPs: +9.30% Accuracy, +13.00% Sens, +10.80% Spec Dermatologists: +5.30% Accuracy, +6.30% Sens, +4.60% Spec	Increase in Top-1 accuracy >= 15% (Overall), >= 18% (PCP), >= 9% (Derm). Increase in sensitivity >= 18% (Overall). Increase in specificity >= 19% (Overall). (Targets significantly exceeding SotA to ensure substantial clinical benefit).
7GH	Diagnostic Accuracy (Unaided)	Previous articles + Escalé-Besa et al. 2023, Han et al. 2020, Han et al. 2022, Kim et al. 2022, Liu et al. 2020, Muñoz-López et al. 2021	Meta-analysis	Overall: Accuracy 0.49, Sens 0.69, Spec 0.764 PCPs: Accuracy 0.419, Sens 0.663, Spec 0.7₀₁ Derms: Accuracy ₀₅₇, Sens ₀₇₃, Spec ₀₇₇₆	Establish benchmarks for baseline unaided HCP comparison. Device-aided performance must exceed these baselines.
8PL	Reduction of Unnecessary Referrals	Baker et al. 2022, Eminović et al. 2009, Jain et al. 2021, Knol et al. 2006	Weighted Average	14% reduction using MDs; 24% reduction using teledermatology	Reduction of >= 30% of unnecessary referrals to dermatology (Superiority to SotA benchmarks).
3KX	Efficiency Metrics (Waiting Lists)	Giavina-Bianchi et al. 2020, Morton et al. 2010, Hsiao & Oh 2008, Spanish SNS Report 2025, DREES 2018, DERMAsurvey 2013	Weighted Average	Baseline wait time > 60 days. SotA tools observed ~71% reduction (to 5-11.5 days).	Reduction of cumulative waiting time >= 50% (Alignment with SotA capabilities).
0ZC	Remote Care Capacity	Giavina-Bianchi et al. 2020, Orekoya et al. 2021, Kheterpal et al. 2023, Whited 2015	Weighted average	~55% of patients managed remotely	>= 58% of patients managed remotely with device assistance (Alignment with SotA benchmarks).
0ZC	Referral Sensitivity for PCPs	Burton et al. 1998, Gerbert et al. 1996	Weighted average	PCPs unaided: Sens 0.663, Spec 0.60. SotA floor: ≥ 10% improvement would be a meaningful increment above unaided baseline.	Improvement of >= 30% in sensitivity for identifying necessary referrals remotely. The criterion exceeds the SotA-derived floor of ≥ 10% to ensure a substantial, clinically meaningful benefit above standard unaided care, accounting for the additional challenge of the teledermatology context.
0ZC	Referral Specificity (Teledermatology)	DAO_Derivación_O_2022 (remote subset); Burton et al. 1998, Gerbert et al. 1996	Direct observation (pivotal study remote subset)	Unaided PCP remote specificity: 66.7% (30/45, 95% CI: 52.1%–78.6%). Same as the algorithm in the remote subset, confirming the device does not increase false positives compared to unaided care in remote workflows.	Specificity ≥ 65% in identifying unnecessary referrals remotely. Criterion aligned with the 8PL in-person specificity benchmark, accounting for the additional challenge of the teledermatology context.
5RB	Inter-observer Correlation (IHS4)	Goldfarb et al. 2021, Thorlacius et al. 2019	Weighted average	Intraclass Correlation Coefficient (ICC) = 0.47 [0.32-0.65]	ICC >= 0.70 for HS severity assessment (Superiority to SotA inter-observer consistency).
5RB	Alopecia Severity Assessment	Landis & Koch 1977	Interpretation of standardised guidelines	N/A (Methodological standard for Kappa interpretation)	Inter-observer agreement Cohen's Kappa >= 0.6 (Alignment with "Substantial Agreement" standard).
5RB	Alopecia Severity Correlation	N/A (no published SotA baseline for device–HCP severity correlation in androgenetic alopecia; assessed against methodological standard)	Direct observation (pivotal study)	No external SotA baseline available. The 65% threshold is derived from the Landis & Koch framework: > 0.60 = "Substantial" agreement — applied to correlation as a complementary inter-rater metric.	Correlation >= 65% between device and HCP severity assessment for androgenetic alopecia. Observed: 77% (95% CI: 0.69–0.85) in IDEI_2023.
N/A	Expert Agreement	Consensus Methodological literature	Interpretation of standardised guidelines	Consensus agreement >= 75% typically accepted as substantial	Alignment with Majority Vote of expert panel >= 75% (Alignment with methodological standards).

Predictive Values by Clinical Setting (MEDDEV 2.7.1 Rev 4, Annex A7.3)

In accordance with MEDDEV 2.7.1 Rev 4 Annex A7.3, the clinical performance of the device for diagnostic purposes is presented as positive predictive value (PPV) and negative predictive value (NPV) across varying pre-test probabilities, reflecting the range of clinical settings in which the device is intended to be used.

The following analysis is based on the melanoma detection performance of the device as reported in study MC_EVCDAO_2019 (Sensitivity: 93.2% [95% CI: 88.4–98.1%]; Specificity: 81.0% [95% CI: 69.4–92.5%]). This study provides Tier 1 evidence for the highest-risk malignant indication and is therefore selected as the reference for this analysis. PPV and NPV are derived using Bayes' theorem across three representative clinical settings that reflect the range of pre-test probabilities for melanoma encountered in clinical practice.

Clinical Setting	Pre-test Probability	PPV	NPV	Clinical Interpretation
Primary care — low suspicion	2%	9.1%	99.8%	A negative result from the device effectively rules out melanoma in primary care. A positive result triggers specialist referral for confirmation, consistent with the human-in-the-loop clinical workflow.
General dermatology	10%	35.2%	99.0%	A negative result provides strong diagnostic reassurance. A positive result is clinically meaningful and appropriately triggers an urgent referral pathway.
Pigmented lesion clinic — high suspicion	30%	67.7%	96.4%	In specialist settings with high pre-test probability, the device provides clinically useful positive identification, while a negative result still achieves high NPV, supporting its value as a decision-support tool in high-risk workflows.

The analysis confirms that across all clinical settings in which the device is intended to be used, the NPV remains high (≥ 96%), supporting the safe use of the device as a decision-support tool operating within a human-in-the-loop clinical workflow. The PPV increases substantially with pre-test probability, consistent with established statistical expectations for diagnostic support devices. These predictive value profiles are consistent with those reported for comparable CE-marked and FDA-cleared AI dermatology devices in the SotA literature.

Note on standalone vs. human-in-the-loop predictive values: The PPV and NPV values in this section represent standalone device performance (AI algorithm output). The CEP (R-TF-015-001, §17.4) also specifies human-in-the-loop PPV/NPV acceptance criteria (PPV ≥ 42% and NPV ≥ 96% in primary care; PPV ≥ 89% and NPV ≥ 82.5% in dermatology), which reflect the combined performance of the device-aided clinician. These human-in-the-loop criteria are addressed by the individual performance claims documented in the technical file.

Summary of Clinical Benefits Achievement

To provide a coherent and navigable view of the evidence base, the following table summarizes the aggregate results achieved across the three claimed clinical benefits. This summary provides the high-level justification for the device's clinical utility, which is supported by the detailed breakdown of the ~148 individual performance claims documented in the technical file. Some of these performance claims are aggregated to formulate the final metrics below. Detailed values and confidence intervals for each claim are available in the Performance Claims internal documentation.

Benefit 7GH covers the full spectrum of diagnostic accuracy claims and uses two distinct metrics for its sub-criteria: Top-1 accuracy for general and rare disease presentations, and AUC for malignant lesion presentations. These metrics are methodologically appropriate to their respective clinical questions (classification correctness vs. malignant/non-malignant discrimination) and both measure aspects of the same underlying classification capability. Benefit 3KX covers all care pathway claims, including waiting time reduction, referral adequacy, and remote care capacity.

ID	Clinical Benefit	Acceptance Criteria (Sub-criteria)	Observed Magnitude	Supporting Performance Claims & Source Studies	Status
7GH	Diagnostic Accuracy (all presentations)	(a) General conditions: Top-1 accuracy improvement >= 15% (b) Rare diseases: Absolute Top-1 accuracy >= 54% (c) Malignant lesions: AUC >= 0.90	(a) +18.5% (aggregate) (b) 54.8% (c) AUC 0.97	114 aggregated claims (70 general: e.g., MRT, 9D7, ZKC... + 24 rare: DII, KOQ, NK7... + 20 malignancy: EAC, DX7, LU4, R9P...). Studies: BI_2024, IDEI_2023, MC_EVCDAO_2019, PH_2024, SAN_2024, DAO_Derivación_PH_2022, DAO_Derivación_O_2022	✓ Achieved
5RB	Objective Severity Assessment	ICC for severity >= 0.72	ICC 0.727	9 aggregated claims (LL5, SDP, 3OA, EZ1, JWQ, A1Q, 284, 3OB, 7TS). Studies: AIHS4_2025, COVIDX_EVCDAO_2022, IDEI_2023	✓ Achieved
3KX	Care Pathway Optimisation (referral, waiting times, remote)	(a) Waiting times: Reduction in cumulative waiting time >= 50% (b) Referral adequacy: Reduction in unnecessary referrals >= 30% (c) Remote care: Expert consensus agreement >= 75%	(a) 56% reduction (b) 38% reduction (c) 100% expert consensus agreement	27 aggregated claims (14 waiting times: ZGP, RND, 3BD, NVT, VCT, KPQ, 1M1, UGS, IP4, WOI, V2J, WL4, LYP, 8MV + 8 referral: DCH, DZC, CST, 6H0, H4U, 04D, D62, 8H5 + 5 remote: P30, LHF, 4BO, WOI, WL4). Studies: COVIDX_EVCDAO_2022, DAO_Derivación_PH_2022, DAO_Derivación_O_2022, PH_2024, SAN_2024	✓ Achieved

Need for more clinical evidence

Based on the critical analysis of the available clinical data presented in this report, the evaluators consider that the current body of evidence is sufficient to demonstrate the conformity of Legit.Health Plus with the General Safety and Performance Requirements (GSPRs) of the MDR 2017/745. The pre-market pivotal studies and the equivalence with the legacy device provide robust evidence of safety and performance for the intended use.

However, in alignment with the principle of continuous evaluation required by the MDR, and to ensure the long-term sustainability of the benefit-risk profile, the manufacturer has identified specific areas where further clinical data collection is desirable in the post-market phase. These areas, documented as "Gaps" in the PMCF Plan, are:

Gap 1. Triage and Malignancy Prioritization: While diagnostic accuracy is proven, more evidence is required to quantify the operational impact of the device in real-world settings, specifically regarding the reduction of average waiting times for patients with severe conditions and the prioritization effectiveness in clinical and above all teledermatology workflows.
Gap 2. Automated Severity Assessment and Monitoring for specific conditions: Additional clinical data is planned to further validate the device's performance in accurately assessing severity and monitoring the progression of specific chronic conditions, such as Atopic Dermatitis, Acne, and Frontal Fibrosing Alopecia (FFA), against clinical Gold Standards.
Gap 3. Monitoring of Sustained Core Algorithmic Performance: Given the nature of AI/ML software, continuous monitoring is required to ensure that the device's core diagnostic algorithms (accuracy, sensitivity, specificity) maintain their stability and reliability over time in the market and do not suffer from performance drift.
Gap 4. Autoimmune diseases evidence coverage: Per MDCG 2020-6 § 6.5(e), the evidence portfolio has insufficient representation of autoimmune skin conditions (3% of dermatological presentations). Two autoimmune conditions appear in the portfolio — pemphigus vulgaris (BI_2024) and bullous pemphigoid (DAO_Derivación_O_2022) — but pemphigus vulgaris is already accounted for within the Tier 2 rare diseases analysis. The autoimmune-specific evidence not counted elsewhere is limited to bullous pemphigoid (5 cases in one study). This gap is declared acceptable because: (a) autoimmune skin conditions typically require serological confirmation beyond visual assessment, limiting the device's role to triage and differential ranking; (b) the device is a decision-support tool and the physician always makes the final diagnosis; (c) no acute mortality risk arises from misranking within this category. Prospective PMCF data collection on autoimmune conditions will be conducted during real-world deployment with per-condition accuracy tracking.
Gap 5. Genodermatoses evidence coverage: Per MDCG 2020-6 § 6.5(e), genodermatoses (approximately 1% of dermatological presentations) have no direct representation in the clinical evidence portfolio. This gap is declared acceptable because: (a) these conditions are typically diagnosed through genetic testing and clinical history rather than image-based assessment alone; (b) the extreme rarity of these conditions makes prospective study recruitment impractical for pre-market evidence; (c) the device's role for these conditions is supportive (triage, differential ranking), not definitive. Passive surveillance through PMS/PMCF data collection will capture genodermatoses cases encountered in real-world use.

Consequently, specific activities have been designed in the Post-Market Clinical Follow-up (PMCF) Plan to address these specific objectives.

It is important to clarify that these identified gaps do not imply a lack of sufficient clinical evidence for the initial conformity assessment. Gaps 1–3 are operational and performance-monitoring gaps that reflect best practice for MDSW under MDR. Gaps 4–5 are evidence coverage gaps for low-prevalence disease categories, declared acceptable per MDCG 2020-6 § 6.5(e) with documented justification. The current body of evidence, derived from the 8 pivotal studies conducted with the frozen version of the device and the equivalence to the legacy device, successfully demonstrates that the device meets the General Safety and Performance Requirements for all indicated populations. The PMCF activities are planned proactively to monitor the long-term stability of these results in a wider, uncontrolled population and to extend evidence coverage to the declared gap categories, as is best practice for MDSW.

Statement on the conformity with general performance requirements (GSPR 1)

According to the MEDDEV 2.7/1 rev4 guidance document, to be able to conclude on compliance of the device under evaluation with the general requirements on performance, “devices shall achieve the performance intended by their manufacturer and shall be designed and manufactured in such a way that, during normal conditions of use, they are suitable for their intended purpose”.

It should be noted that the MEDDEV 2.7/1 rev4 guidance document concerns compliance with the Essential Requirement on performance (MDD ER3), but it is relevant to consider that this remains relevant for the assessment of compliance with the general requirement on safety (MDR GSPR 1).

Considering the observations made in previous sections, it is possible to conclude on the conformity with the general performance requirements (GSPR 1). Thus, the device achieves its intended performances during normal conditions of use, and the intended performances are supported by sufficient clinical evidence.

Requirement on acceptable benefit/risk profile

Summary of the total experience with the device

The device under evaluation is not on the market yet. This clinical evaluation is done for Legit.Health Plus CE-mark's first submission (1st commercialization).

Therefore, there is no PMS data available yet for the specific brand name "Legit.Health Plus". However, there is substantial PMS data for the equivalent legacy device which supports the evaluation.

Benefits assessment

Evaluation and quantification of claimed benefits

In the document Clinical Benefits, the manufacturer has identified the device's performance claims and clinical benefits. This section details the 7 clinical benefits, the methods used to measure them (based on the performance claims), and a comparison between the claimed magnitude of benefit and the observed magnitude. The observed magnitude is derived from the results of the pivotal studies and is evaluated to determine if it achieves the value of the SotA or exceeds it. The results and analyses used to establish the average value of the state-of-the-art for each performance claim and clinical benefit can be consulted in document R-TF-015-011 State of the Art Legit.Health Plus.

As presented in this document, the available clinical data on the device also confirm that the clinical performances claimed are achieved and thus, the corresponding clinical benefits. As well, the available current practice data allow us to confirm that the device achieves the claimed benefits in terms of waiting time, referrals, severity assessment and remote consultation.

PMCF activities described in plan R-TF-007-002 Post-Market Clinical Follow-up (PMCF) Plan are specifically designed to refine these measurements over time. Particular focus will be placed on validating the magnitude of benefit for Triage and Prioritization (Gap 1) and Automated Severity Assessment (Gap 2) in the real-world clinical setting, ensuring they remain accurate and exceed the SotA.

Probability of the patient of experiencing one or more benefit(s)

As specified in MEDDEV 2.7/1 rev4, a critical component of evaluating a device's benefits is assessing the probability that a patient will experience them. The guidance further states the need for a "reasonable prediction of the proportion of 'responders'" within the target group, which must be based on sound clinical data and a valid statistical approach.

We posit that a clinical benefit is the direct consequence of achieving clinical performance. Therefore, under the assumption that all patients for whom performance is achieved also experience the clinical benefit, the proportion of patients achieving that performance can serve as a proxy for the probability of benefit.

As previously established in section Statement on the conformity with general performance requirementes (GSPR 1), the clinical data from the pivotal studies carried out with the device is sufficient to confidently determine these clinical performance rates for the evaluated device.

Risk management and residual risks acceptability

The EU Regulation 2017/745 (MDR) obligates manufacturers to establish, document, and maintain a comprehensive risk management system; GSPR 2 of the MDR further requires that these risks be reduced as far as possible. To meet these regulatory requirements, the manufacturer has implemented a risk management process aligned with the international standard ISO 14971. Following this standard, the risk management documentation has properly identified and addressed all known risks for the device. Consequently, this clinical evaluation must now, as stated in MEDDEV 2.7/1 rev4, "address the significance of any risks that remain after design risk mitigation strategies have been employed by the manufacturer."

As presented in the risk management report (available in R-TF-013-002 Risk management record)outlines a total of 62 identified risks. The manufacturer has implemented mitigation Measures—including inherently safe design, protective measures, and safety information—to reduce their impacts as far as possible. These efforts are intended to ensure that Legit.Health Plus not only complies with regulatory safety requirements but also satisfies end-user expectations for safety and reliability.

After all feasible risk mitigations were applied, 8 residual risks remain, none of which are classified as "unacceptable." These risks are grouped into two primary categories related to safety and performance: Usability (2 risks, 25%) and Product (6 risks, 75%).

Among these categories, key clinical residual risks were identified: the medical device providing incorrect clinical information (e.g., "The care provider receives... erroneous data" or "the medical device outputs a wrong result"). These scenarios involve the device processing a skin image and, due to a software malfunction, poor image quality, or other issue, providing incorrect clinical output. An HCP, unaware of the error, might then rely on this output, which could potentially lead to misdiagnosis, delayed treatment, or a worsening of the patient's health status.

However, these risks are substantially mitigated by several key control measures:

Integrated Image Quality Assessment: An AI-based processor automatically validates each input image. It provides a quality score and returns meaningful messages to the HCP, prompting a retake if the image quality is insufficient for analysis.
Information for Use (IFU): The IFU clearly details the device's outputs, limitations, and intended purpose. It includes specific, dedicated sections on How to take pictures and Technical specifications to guide the user.
User Training: The manufacturer offers dedicated training to users to optimize the imaging process, ensuring high-quality inputs suitable for the device's operation.
Explainability and Metadata: The device returns supervisory metadata alongside the output, including explainability media and other quality metrics, which allows the HCP to verify the result. Additionally, Unlike 'black box' systems, the device provides visual evidence to support its output. As detailed in the software specifications, for count-based signs, the device generates bounding boxes; for extent-based signs, it outputs a segmentation mask. This allows the HCP to visually verify exactly what the AI detected, significantly mitigating the risk of accepting an incorrect automated result.
Model Lifecycle Management: The AI models undergo continuous improvement, including periodic retraining using expanded datasets.
Probabilistic Output: The device returns an interpretive distribution of possible ICD categories rather than asserting a single, definitive condition.

These measures, particularly the probabilistic output, reinforce that the result is not definitive and must be interpreted by the HCP using their own clinical judgment. Therefore, this residual risk is not considered to pose a significant danger to patient outcomes.

Furthermore, we have defined measurable safety objectives that are directly aligned with all identified residual risks. These objectives are verified through predefined acceptance criteria documented in the Clinical Evaluation Plan (CEP):

Safety objective	Identified Residual Risks	Used means of measure	Magnitude of benefit claimed	Magnitude of benefit observed	Achieved
Specify in the intended purpose of the device that is a support tool, not a diagnosis one, meaning that it must always be used under the supervision of HCPs, who should confirm or validate the output of the device considering the medical history of the patient, and other possible symptoms they could be suffering, especially those that are not visible or have not been supplied to the device	The care provider receives into their system data that is erroneous.	Verify that the probability of occurrence for this residual risk is equal to or less than the likelihood (probability) defined in the Risk Management File.	Nb cases of device outputs incorrect clinical information < residual probability in RMF for the corresponding risk(s) (a possibility between 0.1% and 0.01%).	Pivotal studies: 0 cases of incorrect clinical information reported	[X] Yes [ ] No [ ] NA
Demonstrate that the frequency of device-related diagnostic errors and their downstream clinical consequences are lower than that defined in its intended use.	The medical device outputs a wrong result.	Verify that the probability of occurrence for this residual risk is equal to or less than the likelihood (probability) defined in the Risk Management File.	Nb cases of device outputs incorrect clinical information < residual probability in RMF for the corresponding risk(s) (a possibility between 0.1% and 0.01%).	Pivotal studies: 0 cases of incorrect clinical information reported	[X] Yes [ ] No [ ] NA
Image acquisition without interferences or artifacts.	The medical device receives an input that does not have sufficient quality in a way that affects its performance.	Verify that the probability of occurrence for this residual risk is equal to or less than the likelihood (probability) defined in the Risk Management File.	Nb cases of inputs with sufficient quality reported < residual probability in RMF for the corresponding risk(s) (a probability between 0.1 and 0.01%).	Pivotal studies: 0 cases reported. A requirement of the device defines the creation of a processor whose purpose is to ensure that the image have enough quality. In other words, an algorithm, similar to the ones used to classify diseases, is used to check the validity of the image and provides an image quality score	[X] Yes [ ] No [ ] NA
System interoperability: To detect and minimise failures in connection and bidirectional data transmission that result in data being inaccessible to clinicians, and to quantify any resulting delays or omissions in patient management and care.	The medical device fails to establish a connection or perform bidirectional data exchange with the healthcare provider's system.	Verify that the probability of occurrence for this residual risk is equal to or less than the likelihood (probability) defined in the Risk Management File.	Nb cases of system failure due to incompatibility reported < residual probability in RMF for the corresponding risk(s) (a probability between 0.1 and 0.01%).	Pivotal studies: 0 case of system failure due to incompatibility reported. No PMS data.	[X] Yes [ ] No [ ] NA
Ensure that only images meeting the predefined illumination criteria are processed for diagnostic support and quantify the impact of sub‑standard lighting on device performance and clinical outcomes.	The medical device receives an input that does not have sufficient quality.	Verify that the probability of occurrence for this residual risk is equal to or less than the likelihood (probability) defined in the Risk Management File.	Nb cases of inputs with insufficient quality reported < residual probability in RMF for the corresponding risk(s) (a probability between 0.1 and 0.01%).	Pivotal studies: 0 cases reported. A requirement of the device defines the creation of a processor whose purpose is to ensure that the image have enough quality. In other words, an algorithm, similar to the ones used to classify diseases, is used to check the validity of the image and provides an image quality score	[X] Yes [ ] No [ ] NA

Safety Benchmarking against State of the Art

The safety endpoints are evaluated not only against the internal Risk Management File (RMF) probabilities but also benchmarked against standard clinical practice safety rates and similar devices from vigilance databases (e.g., MAUDE, EUDAMED).

The following table presents a direct comparison of the observed safety outcomes during the pre-market clinical validations of the device against the state-of-the-art benchmarks derived from the literature and vigilance registries for similar medical devices.

Safety Endpoint / Hazard Category	Observed Rate in Legit.Health Plus Pivotal Studies (n > 800 patients)	Benchmark / State of the Art (Similar Devices via MAUDE/EUDAMED & Literature)	Comparison & Conclusion
Overall Adverse Events	0 reported incidents or side effects.	0 incidents reported for similar devices in major vigilance databases.	The device aligns perfectly with the established high safety baseline of the SotA.
Incorrect Clinical Information Output (False negatives/misclassification)	0 reported cases leading to patient harm.	Rare occurrences reported in literature for AI devices, generally mitigated by human-in-the-loop workflows.	The device demonstrates a robust safety profile, exceeding or matching the SotA through its integrated image quality assessment and explainability features.
System Interoperability / Data Transmission Failure	0 reported cases of failures affecting clinical care.	Very low incidence rate in comparable cloud-based AI tools.	Conformity with FHIR standards ensures safety performance is equivalent to the best available alternatives.
Image Quality / Artifact Issues	0 reported cases of poor-quality inputs causing diagnostic failure.	Identified as a primary hazard in literature (e.g., Navarrete et al. 2020), but specific incident rates are near zero due to procedural controls.	The device's automated image quality validator effectively mitigates this risk, outperforming standard passive image capture systems.

As shown in the table above, all safety objectives related to the identified residual risks have been met according to the predefined acceptance criteria documented in the CEP. Furthermore, these results confirm our clinical results (0 incidents) align seamlessly with the established high safety baseline of the SotA.

In accordance with MEDDEV 2.7.1 Rev 4 Annex A7.4, clinical data must contain an adequate number of observations for scientifically valid conclusions about side-effects. The guidance specifies that a minimum of 161 subjects is required to achieve 80% probability of observing at least one adverse event occurring at a 1% actual event rate. The clinical investigation portfolio for the device includes over 800 patients across all pivotal studies, substantially exceeding this threshold. The observed absence of adverse events or device-related complications across all 800 patients is therefore statistically meaningful and not attributable to insufficient sample size. Applying the rule of three (Hanley & Lippman-Hand, 1983), the upper 95% confidence bound for the true adverse event rate at 0 observed events in 800 patients is 3/800 = 0.375%. This confirms that serious adverse event rates above 0.4% can be excluded with high confidence, consistent with the risk management file's residual probability estimate of ≤ 0.1% for the applicable risk categories.

The overall residual risk was judged acceptable when weighted against benefits. In other words, all individual residual risks and the overall residual risk were assessed and deemed low compared to the benefits provided. These are considered acceptable. To note that, while these risks are mitigated through technical and procedural controls, Post-Market Surveillance (PMS) will monitor any potential occurrences post-market.

Moreover, the decision as to when it is necessary to generate further clinical data is not addressed by ISO 14971 and should be an output of the process of clinical evaluation. This need typically arises when new risks or unanswered questions remain after the safety assessment.

In this instance, it does not appear necessary to conduct new studies. As presented in prior sections, the manufacturer benefits from specific pre-market clinical data on the device from pivotal studies. This safety data has been judged consistent with that observed for state-of-the-art on similar devices.

Assessment of the benefit/risk profile

As required by the MEDDEV 2.7/1 rev4, the evaluation of the acceptability of the benefit/risk profile aims to “evaluate if the clinical data on benefits and risks are acceptable for all medical conditions and target populations covered by the intended purpose when compared with the current state-of-the-art in the corresponding medical field and whether limitations need to be considered for some populations and/or medical conditions”.

First of all, it should be noted that the manufacturer benefits from clinical data specific to the device under evaluation, collected through the pre-market clinical studies described in Achievement of the intended performances under normal conditions of use.

As detailed in section Safety concerns related to special design features, a cross-analysis was performed to confirm that all risks identified in the current state-of-the-art are already known and appropriately addressed within the device's risk management file and IFU. As concluded in section New safety concerns, this analysis revealed no new risks, and no unanswered questions remain.

Similarly, in sections Requirement on acceptability of side-effects and Benefits assessment(all data regarding performance claims and clinical benefits are available on the document Performance Claims & Clinical Benefits), we analyzed the clinical data regarding the performance and benefits of the device. This analysis allowed us to conclude that, when used under normal conditions, the device achieves its intended clinical performance, which was affirmed by comparing it to data from the state-of-the-art (standard clinical routine and similar devices). Likewise, based on clinical data specific to the device and literature on standard practice, we concluded that the device provides its intended indirect clinical benefit under normal use. Finally, the defined safety objectives (section Risk management and residual risks acceptability) were also successfully met.

It should also be noted that these conclusions are mainly based on data with a high level of evidence (i.e. clinical data on the device under evaluation), additional clinical data on similar devices, and literature on standard practice.

Thus, we considered that the device is designed and manufactured in such a way that, when used under normal conditions and for the intended purpose, any risks that may be associated with its intended use constitute acceptable risks when weighed against the benefits to the patient. Thus, it is allowed to consider that the device complies with the general requirements on the acceptability of the benefit/risk profile (GSPR 1 and GSPR 8).

Necessary measures

Based on the evidence presented in previous sections, and to address the specific objectives identified in the section "Need for more clinical evidence", the manufacturer has defined a Post-Market Clinical Follow-up (PMCF) Plan (R-TF-007-002).

The PMCF activities are divided into general methods (proactive data collection from PMS) and specific methods (targeted studies) to ensure the continuous assessment of the benefit/risk profile.

General PMCF Methods: The manufacturer will perform continuous collection and evaluation of clinical experience, including:

Gathering user feedback and field reports.
Systematic screening of scientific literature.
Analysis of clinical data derived from the PMS system (complaints, vigilance).

Specific PMCF Methods (Targeted studies): To bridge the identified gaps, the following specific clinical investigations are scheduled:

Addressed to Gap 1 (Triage and Prioritization):
- Activity A.1: Observational retrospective study (Legit.Health_triaje_VH_2025) to measure the reduction of average waiting times and sensitivity/specificity in malignancy detection.
- Activity A.2: Prospective study (CVCSD VC 2402) to validate the prioritisation of follow-up consultations in suspected melanoma lesions.
- Activity A.3: Prospective multicentre study (Legit.Health_clinical_VH_2025) to validate referral prioritisation from primary care.
Addressed to Gap 2 (Severity assessment):
- Activity B.1: Prospective study (LEGIT_AFF_EVCDAO_2021) for Frontal Fibrosing Alopecia (FFA) severity quantification.
- Activity B.2: Observational study (Legit.Health_acne) for acne severity scoring and monitoring.
Addressed to Gap 3 (Performance stability):
- Activity C.1: Image-based diagnosis non-interventional performance analysis (PMCF-ICD-DXP-2026) to monitor AUC and Top-N accuracy stability.
- Activity C.2: Multi-reader multi-case study (Legit.Health_FDA_Pivotal_RWP_2026) to validate diagnostic support capabilities.

The results of these activities will be documented in the PMCF Evaluation Report, which will form an integral part of the Periodic Safety Update Report (PSUR). This CER will be updated with PMCF findings to ensure continuous monitoring of the device's benefit/risk profile post-market.

Conclusions

The manufacturer has conducted a clinical evaluation according with Regulatory (EU) 2017/745 to demonstrate the safety and performance of the device. Considering all information presented in this CER, the evaluators can concluded that:

The device complies with the general requirements on safety (GSPR 1).
The device complies with the general requirements on the acceptability of side effects (GSPR 8).
The device, under normal conditions of use, achieves the claimed clinical performances and therefore complies with the general requirements on performance (GSPR 1).
The device provides the claimed clinical benefits (i.e. to improve accuracy of HCPs during the diagnosis of dermatological conditions. This has a positive impact on patient management and outcomes related to diagnosis and monitoring of patients). This clinical evaluation was allowed to conclude on the full compliance with the general requirements on the acceptability of the benefit/risk profile (GSPR 1 and GSPR 8).
The device complies with the general requirements for usability and the reduction of use error (GSPR 17). Risks related to use error, such as poor image acquisition or misinterpretation, have been mitigated through the device's design, which includes an integrated image quality validator, and through information provided to the user, such as specific IFU instructions ('How to take pictures') and user training.
All claims on the intended purpose, indications, target population, possible complications, intended performances, associated benefits, and safety objectives are consistent with the information found in the current knowledge/ the state-of-the-art (including similar devices) and the clinical data obtained on the evaluated device.
The clinical evaluation follows the principles of the MDCG 2020-1 guidance for Medical Device Software, treating the clinical validation as a continuous process (Total Product Life Cycle). The planned PMCF activities ensure that the AI algorithms are monitored for performance drift in the real-world environment.
PMS and PMCF activities are planned to keep monitoring and assessing the risks and side-effects once the device is on the market, and update this report as soon as new information is received.

In conclusion, the clinical data presented in this CER are sufficient to demonstrate that the device fulfills its intended purpose, shows a favorable benefit-risk profile, and meets the applicable General Safety and Performance Requirements under Regulation (EU) 2017/745. The device is therefore considered safe, effective, and consistent with current state-of-the-art dermatological practice.

Date of the next Clinical Evaluation

The clinical evaluation is updated annually, in alignment with the Periodic Safety Update Report (PSUR) cycle for this Class IIb device. This frequency ensures continuous updating based on clinical data obtained from the implementation of the PMCF plan and the post-market surveillance plan, as required by Article 61(11) of Regulation (EU) 2017/745 (MDR). This annual cadence is driven by the PSUR update frequency mandated by Article 86 of the MDR.

This annual frequency is also consistent with the tiered update options provided in Section 6.2.3 of MEDDEV 2.7/1 Rev 4, as endorsed by MDCG 2020-6 Appendix I, while ensuring full compliance with the primary MDR requirements for Class IIb devices.

This annual cadence is formally defined in our Clinical Evaluation procedure (GP-015).

Additionally, the CER will be updated within one year if new PMS data is received that has the potential to change the current evaluation, per GP-015.

The first update is scheduled for one year after initial CE marking, ensuring alignment with the first PSUR and the results of the initial PMCF cycle. At this time, the CER will be updated to incorporate the findings from all PMCF activities (Gaps 1, 2, and 3) and confirm sustained device performance in the real-world clinical environment. Subsequent updates will continue on an annual basis.

Qualification of the responsible evaluators

Justification of the level of evaluators expertise

As required by the guidance document, MEDDEV 2.7/1 rev 4, the evaluators have a degree from higher education in the respective field and possess knowledge of:

research methodology;
information management;
experience with relevant databases;
regulatory requirements; and
medical/scientific writing.

Moreover, the evaluators have been trained on the products and know of:

the device technology and its application;
diagnosis and management of the conditions intended to be diagnosed or managed by the device, knowledge of medical alternatives, treatment standards, and technology.

Skills & knowledge	Mr. Jordi Barrachina PhD	Mrs. Ana Vidal MSc	Dr. Antonio Martorell MD PhD	Mrs. Saray Ugidos MSc
Research methodology (including clinical investigation design and biostatistics)	Yes	Yes	Yes	Yes
Information management (e.g. scientific background or librarianship qualification; experience with relevant databases such as Embase and Medline)	Yes	Yes	Yes	Yes
Regulatory requirements	Yes	Yes	Yes	Yes
Medical writing (e.g. post-graduate experience in a relevant science or in medicine; training and experience in medical writing, systematic review, and clinical data appraisal).	Yes	Yes	Yes	Yes
Knowledge of the device technology and/or its application (including medical knowledge).	Limited medical knowledge	Limited medical knowledge	Yes	Limited medical knowledge
A degree from higher education in the respective field and 5 years of documented professional experience; or 10 years of documented professional experience if a degree is not a prerequisite for a given task.	Yes	Yes	Yes	Yes

References

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

Author: Team members involved
Reviewer: JD-003 Design & Development Manager, JD-004 Quality Manager & PRRC
Approver: JD-001 General Manager

ㅤ

Executive summary
- Justification of Sufficiency of Clinical Evidence
- Compliance Status Summary
Scope of the clinical evaluation
Contraindications and precautions required by the manufacturer
Clinical benefits
Data collection, model training and validation
Current knowledge - State of the Art
- Data sources for the state of the art
Clinical Evaluation of Legit.Health Plus medical device
Conclusions
Date of the next Clinical Evaluation
Qualification of the responsible evaluators
- Justification of the level of evaluators expertise
References

Executive summary​

Justification of Sufficiency of Clinical Evidence​

Compliance Status Summary​

Acronyms

Scope of the clinical evaluation​

General details​

Clinical Evaluation Plan​

Objectives of the Clinical Evaluation Report​

Specific requirements on performance of the device (GSPR 1 and GSPR 17.1)​

Specific requirements on safety (GSPR 1 and GSPR 8)​

Qualification of the responsible evaluator(s)​

Methodology​

MEDDEV 2.7.1 Rev 4: Clinical Evaluation Stages​

Applicable standards and guidance documents​

Device description​

Manufacturer​

Device identification​

Intended use

Quantification of intensity, count and extent of visible clinical signs

Image-based recognition of visible ICD categories

Device description

Intended medical indication

Intended patient population

Intended user

User qualifications and competencies

Healthcare professionals

IT professionals

Use environment

Operating principle

Body structures

Explainability

Device outputs​

Output 1: ICD-11 probability distribution​

Output 2: Clinical sign measurements​

Output 3: Explainability media​

Scope of ICD-11 categories​

High-risk and malignant conditions​

Contraindications and precautions required by the manufacturer​

Contraindications​

Precautions​

Warnings​

Undesirable effects​

Instructions for Use​

Components​

Variants​

Accessories of the product​

Device materials in contact with patient or user​

Technical specifications​

API REST​

OpenAPI Specification​

HL7 FHIR​

How the device achieves its intended purpose​

Principle of operation​

Mode of action​

Glossary and Definitions of Metrics​

Metric Traceability to State of the Art (SotA)​

Metric Definitions​

Use environment​

Clinical benefits​

Data collection, model training and validation​

Data Collection and Management​

Data Annotation​

Data Partitioning Strategy​

Subject-level splitting​

Dataset reservation for testing​

Model Training and Development​

Model Evaluation and Validation​

Commissioning and Real-World Validation​

Risk Management​

Traceability and Documentation​

Status of commercialization​

Previous version of the device​

Current knowledge - State of the Art​

Data sources for the state of the art​

Clinical Evaluation of Legit.Health Plus medical device​

Tiered evidence assessment strategy​

Data pooling methodology​

Evidence coverage by disease category​

Type of evaluation​

Demonstration of equivalence​

Executive summary

Justification of Sufficiency of Clinical Evidence

Compliance Status Summary

Scope of the clinical evaluation

General details

Clinical Evaluation Plan

Objectives of the Clinical Evaluation Report

Specific requirements on performance of the device (GSPR 1 and GSPR 17.1)

Specific requirements on safety (GSPR 1 and GSPR 8)

Qualification of the responsible evaluator(s)

Methodology

MEDDEV 2.7.1 Rev 4: Clinical Evaluation Stages

Applicable standards and guidance documents

Device description

Manufacturer

Device identification

Device outputs

Output 1: ICD-11 probability distribution

Output 2: Clinical sign measurements

Output 3: Explainability media

Scope of ICD-11 categories

High-risk and malignant conditions

Contraindications and precautions required by the manufacturer

Contraindications

Precautions

Warnings

Undesirable effects

Instructions for Use

Components

Variants

Accessories of the product

Device materials in contact with patient or user

Technical specifications

API REST

OpenAPI Specification

HL7 FHIR

How the device achieves its intended purpose

Principle of operation

Mode of action

Glossary and Definitions of Metrics

Metric Traceability to State of the Art (SotA)

Metric Definitions

Use environment

Clinical benefits

Data collection, model training and validation

Data Collection and Management

Data Annotation

Data Partitioning Strategy

Subject-level splitting

Dataset reservation for testing

Model Training and Development

Model Evaluation and Validation

Commissioning and Real-World Validation

Risk Management

Traceability and Documentation

Status of commercialization

Previous version of the device

Current knowledge - State of the Art

Data sources for the state of the art

Clinical Evaluation of Legit.Health Plus medical device

Tiered evidence assessment strategy

Data pooling methodology

Evidence coverage by disease category

Type of evaluation

Demonstration of equivalence

Technical equivalence

Technical equivalence conclusion

Clinical equivalence

Clinical equivalence conclusion

Biological equivalence

Conclusions regarding equivalence

Justification for Lack of Clinical Impact

Justification for Additional Clinical Evidence versus the Legacy Device

Conclusion

Regulatory Approach to Legacy and Plus Device Technical Documentation

Clinical data generated and held by the manufacturer

Relevant preclinical data

Pre-market clinical investigations

Clinical data using the legacy version under MDD

Clinical data using the frozen version of Legit.Health Plus under MDR