R-TF-015-003 Clinical Evaluation Report

Table of contents

• Executive summary
• Scope of the clinical evaluation
  • General details
  • Clinical Evaluation Plan
  • Objectives of the Clinical Evaluation Report
  • Qualification of the responsible evaluator(s)
  • Methodology
  • Applicable standards and guidance documents
  • Device description
    • Manufacturer
    • Device identification
    • Contraindications and precautions required by the manufacturer
    • Warnings
    • Undesirable effects
    • Instructions for Use
    • Components
    • Variants
    • Accessories of the product
    • Device materials in contact with patient or user
    • Technical specifications
    • How the device achieves its intended purpose
    • Use environment
    • Clinical benefits
    • Data collection, model training and validation
    • 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
  • Type of evaluation
  • Demonstration of equivalence
    • Technical equivalence
    • Clinical equivalence
    • Biological equivalence
    • Conclusions regarding equivalence
    • Justification for Additional Clinical Evidence versus the Legacy Device
    • 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 generated from risk management and PMS activities
  • Clinical data collected from literature search
    • Literature search plan
    • Selection of references relating to the device under evaluation
    • Appraisal of the clinical data relating to the device under evaluation
    • Results of the literature search on the device under evaluation
  • Analysis of the clinical data
    • Requirement on safety
    • Requirements on acceptability of side-effects
    • Requirement on performance
    • Requirement on acceptable benefit/risk profile
  • Necessary measures
• 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, 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 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 the 8 pre-market pivotal studies carried out and with a high level of evidence based on the MDCG 2020-6 guidance along with the study performed with the legacy device.

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.

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 comply with the general requirements on performances (GSPR 1).

Based on data from risk management, data observed 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

Acronyms	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”, or more precisely on the acceptability of all known and foreseeable risks and any undesirable side-effects, when weighed against the evaluated benefits to the patient and/or user” (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).

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

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

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, enhancing efficiency and accuracy of care delivery, by providing:

• 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,
• 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

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
          }
        }
      ]
    }
  }
}

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 sympthoms 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 quantity the intensity of visual clinical signs like erythema, excoriation, dryness, lichenification, oozing, and edema.

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.

Clinical benefits

For more information regarding the evaluation and qualification of claimed benefits of the device, please refer to the document Clinical Benefits.

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:

1. Training set: Used to fit or train the parameters of the AI model
2. Validation set: Used to provide an unbiased evaluation of the model fit on the training set while tuning model hyperparameters
3. 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. This earlier version was designed to provide a standalone interface as well as an API, allowing users to engage with it independently of their existing Electronic Health Records. However, this design direction was later recognized as suboptimal, as most organizations expressed a preference for integrating the device's functionalities directly into their existing systems.

In other words: the new generation differs from the previous device in that it's meant to integrate into organisation's softwares. It is used server-to-server, by computer programs. This means that the new device is simpler and contains less elements. This design allows us to focus on other issues, such as interoperability and documentation. And it allows us to invest our development efforts into the scalable architecture of the device, the structure of the input and the output, and helping customers during the integration process.

Legit.Health has been commercialized since 2020 (after obtaining the manufacturing license in Spain) and was certified under the Medical Devices Directive (MDD). Since that time, we've partnered with 21 diverse customers. The customers span from goverment-run care providers to for-profit care providers. Over this period, more than 4,500 diagnostic reports have been crafted by more than 500 professionals. They've utilized our product to help over a thousand patients.

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 = 228 candidate records. After de-duplication and multi-stage screening, n = 57 clinical articles were included and appraised for methodological quality and relevance. An additional n = 10 items (primarily two manuscripts, 9 guidelines and contextual documents) were referenced to inform clinical context; total material considered = 68. Breakdown used for appraisal: 58 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 = 53): mean weight = 6.88 / 10. Additional metrics: mean relevance = 4.40 / 6; mean quality = 2.47 / 4; mean level of clinical evidence = 6.3 / 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
s 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

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:

1. 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;

2. a critical evaluation of the results of all available clinical investigations, [...]; and

3. 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—mainly related to software version stabilization and the consolidation of features—are not expected to negatively affect safety or performance. On the contrary, these changes aim to facilitate conformity under the MDR by freezing the functionality and maintaining the same 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:

1. 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.

2. 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 (available in CONCRETAR). 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 to support clinical safety and performance of the device and to generate the the necessary clinical data. Along with this, 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.
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-15-006 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 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, but the final analysis was carried out wth 117. 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 (48%) and 16 female (52%). 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 33.33%, II 40.01%, III 23.33% and IV 3.33% 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-15-006 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-15-006 Clinical Investigation Report).

Clinical data generated from risk management and PMS activities

Complaints regarding the safety and performance of the evaluated device

Once on the market, to receive information regarding the safety and the performance of the device, the manufacturer AI Labs Group S.L. will 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 in future updates of this CER.

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.

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.

As this data is preclinical in nature and does not report on the device's performance in an actual clinical setting, these articles are not suitable for inclusion in this clinical evaluation. This information is appropriately addressed in other sections of the Technical File (e.g., preclinical verification and validation)

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).

Safety concerns related to special design features

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 tabl 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.

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.

Consequently, specific activities has 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. The current body of evidence, derived from the 8 pivotal studies and the equivalence to the legacy device, successfully demonstrates that the device meets the General Safety and Performance Requirements. The PMCF activities are planned proactively to monitor the long-term stability of these results in a wider, uncontrolled population, 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.

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 sympthoms 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

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.

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.

1. 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).

2. 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).

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 [from user prompt].
• 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

According to section 6.2.3 of MEDDEV 2.7/1 revision 4 (June 2016) in the Guidelines on Medical Devices, the clinical evaluation is actively updated:

• when the manufacturer receives new information from PMS that has the potential to change the current evaluation;
• if no such information is received, then
  • at least annually if the device carries significant risks or is not yet well established; or
  • every 2 to 5 years; if the device is not expected to carry significant risks and is well established, a justification should be provided.

The PMCF Evaluation Report, which forms an integral part of the Periodic Safety Update Report (PSUR) for this Class IIb device, is estimated to be completed by January 2027.

Thus, unless the manufacturer receives new information from the implementation of the PMS and PMCF plans requiring the immediate update of this clinical assessment (such information can be the evolution of the benefit/risk profile, newly emerged side effect), the next update of this Clinical Evaluation Report is scheduled for January 2027, ensuring alignment with the PSUR and the results of the first PMCF cycle.

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, JD-004
• Approver: JD-005