R-TF-015-001 Clinical Evaluation Plan

Table of contents

Purpose
Scope of the clinical plan as part of the clinical evaluation
References
Acronyms and definitions
- Acronyms
- Definitions
Responsibilities - Competence of the Clinical Evaluation Team
Identification of relevant product requirements
Description
Risk management
State of the art
Clinical Development Plan
Clinical Evidence
Clinical Concerns
Annexes

Purpose

Article 61(3) of the MDR 2017/745 states that a clinical evaluation must “follow a defined and methodologically sound procedure”, meaning that a Clinical Evaluation Plan needs to be established in advance and should define how the evaluation shall be conducted. The MDR Annex XIV Part A provides further details on requirements for CEP.

This Clinical Evaluation Plan (CEP) is dedicated to stage 0 of the clinical evaluation process, adhering closely to the requirements outlined in the Medical Devices Regulation (MDR) 2017/745 and the guideline on medical devices and clinical evaluation (MEDDEV 2.7/1 rev4).

The evaluation and determination of clinical data, studies and relevant observations to consider when providing and showing conformity with Regulation (EU) 2017/745 General Safety & Performance Requirements number #1, #8 and #17 (requiring support from the clinical data), has been performed according to:

Requirements from Article 61 and Annex XIV from Regulation (EU) 2017/745.
Recommendations from the MDCG guidelines MDCG 2020-13, MDCG 2020-1 and MDCG 2020-5.
Recommendations from the MEDDEV 2.7/1 Rev.4 guideline.

The clinical evaluation that we develop and discuss through this Clinical Evaluation Plan (CEP) consolidates the evaluation of the collected clinical data related to the product Legit.Health Plus (hereinafter, "the device").

This Clinical Evaluation Plan has the following purposes:

Evaluating the conformity of the product towards the General Safety and Performance Requirements (GSPR) requiring clinical evidence from Regulation (EU) 2017/745.
The evaluation of the clinical data compiled in order to demonstrate the acceptance of its valid clinical association, the technical performance and the clinical performance of the MDSW.
Determining the evaluation and acceptability of the benefit-risk ratio in the conditions when the product is used according to its intended use and indications, stated in its technical documentation.
The compilation of all the clinical data collected through clinical investigations with the product, systematic bibliographic research of clinical literature, and information related to the product risk management, with special emphasis to the finding and management of the identification of unknown risks and/or secondary effects.

This plan applies to the device. The product is classified as a class IIb medical device. The legacy device, Legit.Health, has been commercialized since 2020 under the Medical Devices Directive (MDD) 93/42/EEC. The product 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.

This Clinical Evaluation Plan will be checked and, if necessary, updated in each milestone and/or review of the Legit.Health Plus project.

Scope of the clinical plan as part of the clinical evaluation

The clinical evaluation is based on a comprehensive analysis of available pre- and post-market clinical data that is relevant to the intended purpose of the device, including clinical performance data and clinical safety data.

There are discrete stages in performing a clinical evaluation:

Stage 0: Define the scope, plan the clinical evaluation (also referred to as scoping and the clinical evaluation plan).
Stage 1: Identify pertinent data.
Stage 2: Appraise each individual data set, in terms of its scientific validity, relevance and weighting.
Stage 3: Analyze the data, whereby conclusions are reached about
- compliance with general safety and performance requirements, including its benefit/risk profile,
- the contents of information materials supplied by the manufacturer (including the label, IFU of the device, available promotional materials, including accompanying documents possibly foreseen by the manufacturer),
- residual risks and uncertainties or unanswered questions (including rare complications, long-term performance, safety under widespread use), whether these are acceptable for CE-marking, and whether they are required to be addressed during PMS.
Stage 4: Finalize the Clinical Evaluation Report.

Before a clinical evaluation is undertaken, the manufacturer should define its scope based on the General Performance and Safety Requirements that need to be addressed from a clinical perspective and the nature and history of the device.

The scope serves as a basis for further steps, including identifying pertinent data. The manufacturer sets up a description of the device under evaluation and a clinical evaluation plan.

Depending on the stage in the lifecycle of the product, considerations for setting up the Clinical Evaluation Plan should include the following different aspects:

The device description.
Whether there are any design features of the device, or any indications or target populations, require specific attention.
Information needed for evaluation of equivalence if equivalence may be claimed.
Information from similar devices from the market.
The risk management documents of the device.
The current knowledge/ state of the art in the corresponding medical field.
Data source(s) and type(s) of data to be used in the clinical evaluation.
Whether the manufacturer has introduced / intends to introduce any relevant changes.
Whether any specific clinical concerns have newly emerged and need to be addressed.
PMS aspects - recall from similar devices.
Needs for planning PMS activities: PMCF.

The scope of the CEP is limited to the device.

References

Reference document code	Reference document description
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
2023/C 163/06	Commission Guidance on the content and structure of the summary of the clinical investigation report
MDCG 2020-10/1 Rev.1MDCG 2020-10/2 Rev. 1	Guidance on safety reporting in clinical investigationsAppendix: Clinical investigation summary safety report form
MDCG 2020-1	Guidance on clinical evaluation (MDR) / Performance evaluation (IVDR) of medical device software
MDCG 2022-21	Guidance on Periodic Safety Update Report (PSUR) according to Regulation (EU) 2017/745 (MDR)
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 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
IEC 82304-1:2017	Part 1: General requirements for product safety
UNE-EN 62304:2007/A1:2016 (EN 62304:2006/A1:2015)	Medical device software - Software life-cycle processes

Acronyms and definitions

Acronyms

Acronyms	Definition
CEP	Clinical Evaluation Plan
CER	Clinical Evaluation Report
CIP	Clinical Investigation Plan
CIR	Clinical Investigation Report
EU/EC	European Union / European Community
EMDN	European Medical Devices Nomenclature
FSCA	Field Safety Corrective Action
GSPR	General Safety and Performance Requirement
IFU	Instructions for Use
MDD	Medical Devices Directive
MDR	Medical Device Regulation
PICO	Population/ people/patient/ problem, Interventions, Comparison and Outcome
PMS	Post-Market Surveillance
PMCF	Post-Market Clinical Follow Up
QMS	Quality Management System
SotA	State of the Art
SRN	Single Registration Number
UDI/DI	Unique Device Identification / Device Identifier

Definitions

The clinical evaluation is using conventional terms defined and used in the reference texts. Some terms, particularly important in the context of clinical evaluation, are defined below.

Term	Definition
Benefit / Risk Determination	The analysis of all assessments of benefit and risk of possible relevance for the use of the device for the intended purpose, when used in accordance with the intended purpose given by the manufacturer (MDR, Article 2(24)).
Clinical Data	Information concerning safety or performance that is generated from the use of a device and is sourced from the following: clinical investigation(s) of the device concerned, clinical investigation(s) or other studies reported in scientific literature of a device for which equivalence to the device in question can be demonstrated, reports published in the peer-reviewed scientific literature on other clinical experience of either the device in question or a device for which equivalence to the device in question can be demonstrated, clinically relevant information coming from post-market surveillance, in particular, the post-market clinical follow-up (MDR).
Clinical Development Plan	A plan that indicates progression from exploratory investigations, such as first-in-man studies, feasibility and pilot studies, to confirmatory investigations, such as pivotal clinical investigations, and a PMCF for a device or product under evaluation (MDR Annex XIV PART A 1(a)).
Clinical Evaluation	A systematic and planned process to continuously generate, collect, analyze and assess the clinical data pertaining to a device in order to verify the safety and performance, including clinical benefits, of the device when used as intended by the manufacturer (MDR). A methodologically sound ongoing and continuous procedure to collect, appraise, and analyze clinical data pertaining to a medical device and to analyze whether there is sufficient clinical evidence to confirm compliance with relevant Essential Requirements or Principles for clinical safety and performance of that device when used in accordance with the manufacturer's instructions for use (MEDDEV 2.7/1).
Clinical Evidence	Clinical data and clinical evaluation results pertaining to a device of sufficient amount and quality to allow a qualified assessment of whether the device is safe and achieves the intended clinical benefit(s) when used as intended by the manufacturer (MDR).
Clinical Performance	Article 2 (52) MDR defines clinical performance as the ability of a device, resulting from any direct or indirect medical effects which stem from its technical or functional characteristics, including diagnostic characteristics, to achieve its intended purpose as claimed by the manufacturer, thereby leading to a clinical benefit for patients, when used as intended by the manufacturer (MDCG 2020-1).
Clinical Safety	Freedom from unacceptable clinical risks, when using the device per the manufacturer's Instructions for Use (MEDDEV 2.7/1). According to MDR, Article 62 (1), the clinical investigation shall be designed with the purpose of verifying the clinical safety of the device and to determine any undesirable side-effects, under normal conditions of use of the device, and assess whether they constitute acceptable risks when weighed against the benefits to be achieved by the device (MDR).
Intended Purpose	The use for which a device is intended according to the data supplied by the manufacturer on the label, in the instructions for use or in promotional or sales materials or statements and as specified by the manufacturer in the clinical evaluation (MDR).
Post-Market Clinical Follow-Up (PMCF) Plan	A PMCF plan shall specify the methods and procedures established by the manufacturer to proactively collect and evaluate clinical data from the use of a CE-marked medical device in or on humans, placed on the market or put into service within its intended purpose, as referred to in the relevant conformity assessment procedure. The PMCF plan aims to confirm the safety (including the acceptability of identified risks, particularly residual risks) and performance, including the clinical benefit if applicable, of the device throughout its expected lifetime; identify previously unknown side effects; and monitor the identified side effects and contraindications. Identifying and analyzing emergent risks on the basis of factual evidence; ensuring the continued acceptability of the benefit-risk ratio, referred to in Sections 1 and 9 of Annex I in the MDR. Identifying possible systematic misuse or off-label use of the device, with a view to verifying that the intended purpose is correct.
PMCF Study	A study carried out following marketing authorization intended to answer specific questions (uncertainties) relating to safety, clinical performance and/or effectiveness of a device when used in accordance with its labelling (IMDRF MDCE WG/N65FINAL:2021).
Post-Market Surveillance (PMS)	All activities carried out by manufacturers in cooperation with other economic operators to institute and keep up to date a systematic procedure to proactively collect and review experience gained from devices they place on the market, make available on the market or put into service for the purpose of identifying any need to immediately apply any necessary corrective or preventive actions (MDR).
Risk	Combination of the probability of occurrence of harm and the severity of that harm (MDR).
Risk Management	Systematic application of management policies, procedures and practices to the tasks of analyzing, evaluating, controlling and monitoring risk. Risk assessments should document intended as well as reasonably foreseeable misuse (ISO 14971).
State of the Art	Developed stage of current technical capability and/or accepted clinical practice in regard to products, processes and patient management, based on the relevant consolidated findings of science, technology and experience. Note: The STATE OF THE ART embodies what is currently and generally accepted as good practice in technology and medicine. The state of the art does not necessarily imply the most technologically advanced solution. The STATE OF THE ART described here issometimes referred to as the “generally acknowledged STATE OF THE ART”.
Technical Performance	Capability of a MDSW to accurately and reliably generate the intended technical/analytical output from the input data (MDCG 2020-1).
Valid Clinical Association	Means the association of an MDSW output with a clinical condition or physiological state (MDCG 2020-1).

Responsibilities - Competence of the Clinical Evaluation Team

The clinical evaluation should be conducted by a suitably qualified individual or a team.

In accordance with Regulation (EU) 2017/745, Annex XIV, Part A, section 1(d), the following section provides information about the team responsible for the preparation of the Clinical Evaluation Report (CER), along with justification of their qualifications and suitability to conduct the clinical evaluation.

Name	Role in the Evaluation	Academic Degree	Relevant Experience	Justification of Suitability
Jordi	Clinical Affairs Manager	PhD	4 years	He has a long experience in clinical research and the design of clinical validations, especially with medical devices. He is familiar with the process of literature review and medical writing, following regulatory requirements according to MDR.
Ana	Independent Reviewer	MSc	6 years	Her extensive background provides the necessary expertise, encompassing a deep understanding of the MDR regulatory framework, proficiency in clinical evaluation methodology (including systematic literature reviews and benefit-risk analysis), and the technical competence to assess Software as a Medical Device (SaMD).
Saray	Quality/Regulatory Reviewer	MSc	8 years	She has over 8 years of experience in the medical device industry, with a strong understanding of regulatory requirements and clinical best practices, comprehensive knowledge of the product and its clinical context, relevant academic and regulatory training, and proven experience in drafting clinical evaluation documents in compliance with MDR
Antonio	Subject Matter Expert (SME)	PhD, MD	17 years	The SME is a board-certified dermatologist with over 10 years of clinical experience, possessing extensive knowledge of the device's intended use and therapeutic context, along with strong expertise in evaluating clinical evidence, and a proven track record in contributing to clinical evaluations in accordance with MDR requirements

All team members have been selected based on their academic background, clinical expertise, and regulatory experience relevant to the medical device under evaluation. Supporting evidence is provided in Annex I, including:

CVs of each team member.
Certificates of MDR training, CER writing, and literature review methodology.
Declarations of Potential Conflict of Interest.

Identification of relevant product requirements

In view of the willingness to demonstrate the scientific validity of the exposed data, it is essential to elaborate a clear and methodological plan for the identification, retrieval, appraisal and weighting of clinical data. The endpoints must be consistent with currently accepted scientific standards and must be justified according to the State of the Art (SotA).

Taking into account the regulatory status of the product under the scope of the clinical evaluation, the organization considers the applicable General Safety and Performance Requirements (GSPR) by demonstrating its compliance with the product by using clinical data providing sufficient clinical evidence. The level of clinical evidence will be appropriate considering the risk class, intended use and characteristics of the product under the scope.

According to the MDCG 2020-1 guideline, there are three key components to be taken into account when compiling clinical evidence:

Valid clinical association is understood as the extent to which the MDSW's output (e.g. concept, conclusion, calculations) based on the inputs and algorithms selected, is associated with the targeted physiological state or clinical condition. This association should be well-founded or clinically accepted. The valid clinical association of an MDSW should demonstrate that it corresponds to the clinical situation, condition, indication or parameter defined in the intended purpose of the MDSW.
Validation of the technical performance is the demonstration of the MDSW's ability to accurately, reliably and precisely generate the intended output, from the input data.
Validation of the clinical performance is the demonstration of a MDSW's ability to yield clinically relevant output in accordance with the intended purpose. The clinical relevance of a MDSW's output is a positive impact
- On the health of an individual expressed in terms of measurable, patient-relevant clinical outcome(s), including outcome(s) related to diagnosis, prediction of risk, prediction of treatment reponse(s), or
- Related to its function, such as that of screening, monitoring, diagnosis or aid to diagnosis of patients, or
- On patient management or public health.

As a minimum clinical data will be used to show that the device is safe, has an acceptable benefit/risk profile, performs as intended (GSPR 1), and has an acceptable side-effect profile (GSPR 8) as outlined in Annex I of the MDR 2017/745 and in the following table.

ID	Annex I Requirement	Justification
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. They 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.	According to the Regulation (EU) 2017/745, the evaluation of the clinical performance and safety as well as the clinical benefit must be based on ‘clinical data’ and is required for all medical device classes, consequently clinical investigations are essential to prove this requirement.
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.	According to the Regulation (EU) 2017/745, the evaluation of the undesirable side-effects and of the acceptability of the benefit- risk- ratio shall be based on clinical data providing sufficient clinical evidence.
17	(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. (17.2) For devices that incorporate software or for software that are devices in themselves, the software shall be developed and manufactured in accordance with the state of the art taking into account the principles of development life cycle, risk management, including information security, verification and validation. (17.3) Software referred to in this Section that is intended to be used in combination with mobile computing platforms shall be designed and manufactured taking into account the specific features of the mobile platform (e.g. size and contrast ratio of the screen) and the external factors related to their use (varying environment as regards level of light or noise).	17.1: clinical reliability and repeatability can only be demonstrated with clinical data. "Performance in line with their intended use" is, by definition, the validated clinical performance proven in a study. 17.2: According to the Regulation (EU) 2017/745, the validation phase of the software development lifecycle must include clinical validation. The planned clinical investigation is the primary means of fulfilling this requirement. 17.3: This sub-point requires accounting for the specifics of mobile platforms (e.g., screen size, ambient light). A clinical investigation is the ultimate test to prove that the device's performance is maintained in these variable, real-world usage scenarios.

Description

The description written in this section is detailed enough to allow for a valid evaluation of the state of compliance with GSPR, the retrieval of meaningful literature data, and the assessment of equivalence to other devices described in the scientific literature.

Legit.Health Plus is a Class IIb device with Rule 11 applied in accordance with Annex VIII Chapter III of Regulation (EU) 2017/745 of the Council of the European Union of 5 April 2017. The product is a legacy device which has been marketed since 2020 following the acquisition of its Spanish manufacturing license, and has undergone continuous evaluation through post-market activities.

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

Manufacturer identification

	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

Intended use

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

Quantification of intensity, count and extent of visible clinical signs

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

erythema,
desquamation,
induration,
crusting,
xerosis (dryness),
swelling (oedema),
oozing,
excoriation,
lichenification,
exudation,
wound depth,
wound border,
undermining,
hair loss,
necrotic tissue,
granulation tissue,
epithelialization,
nodule,
papule
pustule,
cyst,
comedone,
abscess,
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.

Intended clinical benefits

The device provides clinical benefits by enhancing the precision and efficiency of dermatological assessments through advanced image analysis of visible skin structures. By quantifying the intensity, count, and extent of clinical signs, it offers detailed and consistent data, which aids healthcare providers in evaluating a wide range of skin conditions across the epidermis, appendages, associated mucous membranes, dermis, cutaneous vasculature, and subcutis.

Additionally, it interprets and maps possible ICD classifications, streamlining clinical workflows and supporting standardized assessments for more accurate, data-driven patient care. All in all, this lower waiting times for specialist consultation, reduces time to diagnosis by facilitating clinical information and reduces social cost and improves the dermatological standard and care. All intended clinical benefits with relevant outcome measures related to the device can be found in the table below.

The table below defines, for each claimed benefit, the means of measure and the magnitude of benefit claimed set by the manufacturer to judge whether it has been achieved or not.

ID	Intended Clinical Benefits	Means of measure	Magnitude of benefit claimed
7GH	The device improves 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.	- Improvement in Top-1 accuracy of all HCP tiers - Top-1 accuracy of all HCP tiers using the device - Improvement in Top-1 sensitivity of all HCP tiers - Top-1 sensitivity of all HCP tiers using the device - Top-1 specificity of all HCP tiers using the device - Improvement in Top-1 specificity of all HCP tiers - Improvement in Top-1 accuracy of GPs - Top-1 accuracy of the GPs using the device - Improvement in Top-1 sensitivity of GPs - Top-1 sensitivity of GPs using the device - Improvement in Top-1 specificity of GPs - Top-1 specificity of GPs using the device - Improvement in Top-1 accuracy of the dermatologists - Top-1 accuracy of the dermatologists using the device - Improvement in Top-1 sensitivity of dermatologists - Top-1 sensitivity of dermatologists using the device - Improvement in Top-1 specificity of dermatologists - Top-1 specificity of dermatologists using the device	- An increase of 15% in the diagnostic accuracy of dermatological conditions across all HCP tiers. - A diagnostic accuracy with the assistance of the device of at least 71% across all HCP tiers. - An increase of 18% in the diagnostic sensitivity of dermatological conditions across all HCP tiers. - A diagnostic sensitivity with the assistance of the device of at least 74% across all HCP tiers. - An increase of 19% in the diagnostic specificity of dermatological conditions across all HCP tiers. - A diagnostic specificity with the assistance of the device of at least 78% across all HCP tiers. - An increase of 18% in the diagnostic accuracy of dermatological conditions in primary care. - A diagnostic accuracy with the assistance of the device of almost 71% in primary care. - An increase of 19% in the diagnostic sensitivity of dermatological conditions in primary care - A diagnostic sensitivity with the assistance of the device of at least 74% in primary care. - An increase of 20% in the diagnostic specificity of dermatological conditions in primary care. - A diagnostic specificity with the assistance of the device of at least 80% in primary care. - An increase of 9% in the diagnostic accuracy of dermatological conditions in dermatology. - A diagnostic accuracy with the assistance of the device of at least 73% in dermatology. - An increase of 10% in the diagnostic accuracy of dermatological conditions in dermatology. - A diagnostic sensitivity with the assistance of the device of at least 76% in dermatology. - An increase of 10% in the diagnostic specificity of dermatological conditions in dermatology. - A diagnostic specificity with the assistance of the device of at least 82% in dermatology.
3KX	The device reduces waiting times for skin-related medical consultations by providing healthcare providers with additional relevant clinical information. This has a positive impact on patient management and outcomes related to the diagnosis and monitoring of patients.	- Reduction in cumulative waiting time in percentage to see the specialist - Reduction in cumulative waiting time to see the specialist compared to the region - The experts consider that the use of the device enables specialists to complete medical consultations in less time compared to standard practice.	- A reduction of cumulative waiting time for patients to access specialist dermatological care of at least 50%. - A reduction of cumulative waiting time in days to access specialist dermatological care. - More than 70% of experts state that the use of the device allow them to handle consultats in 5 to 10 minutes and reduce the time needed.
8PL	The device improves the precision of HCPs during the referral. This has a positive impact on patient management, increases the adequacy of referrals and positively impacts outcomes related to diagnosis and monitoring of patients.	- Reduction in unnecessary referrals without increasing false negatives - Sensitivity to identify necessary referrals - Specificity to identify unnecessary referrals	- A reduction of at least 30% of unnecessary referrals to dermatology. - A sensitivity of at least 70% identifying patients who require dermatological referral. - A specificity of at least 65% identifying patients who do not require dermatological referral.
1QF	The device improves accuracy of HCPs during the diagnosis of lesions suspicious for skin cancer. This has a positive impact on patient management and outcomes related to diagnosis and monitoring of patients, such as reducing the need for invasive procedures.	- Area Under the ROC Curve detecting malignancy - Sensitivity detecting malignancy - Specificity detecting malignancy - Positive Predictive Value in primary care - Negative Predictive Value in primary care - Positive Predictive Value in dermatology - Negative Predictive Value in dermatology - Area Under the ROC Curve detecting melanoma - Accuracy detecting melanoma - Sensitivity detecting melanoma - Specificity detecting melanoma	- An AUC of at least 90% detecting malignant conditions. - A sensitivity of 79% detecting malignant conditions. - A specificity of 87% ruling out malignant conditions. - A PPV in primary care of 42%. - A NPV in primary care of 96%. - A PPV in dermatology of 89%. - An NPV in dermatology of 82.5% - An AUC of 85% detecting melanoma. - A sensitivity of 93% detecting melanoma. - A specificity of 80% ruling out melanoma. - An accuracy of 81% detecting melanoma.
9VW	The device improves accuracy of HCPs during the diagnosis of rare diseases. This has a positive impact on patient management and outcomes related to diagnosis and monitoring of patients, especially those suffering from rare diseases.	- Improvement in Top-1 accuracy of all HCP tiers in rare diseases - Top-1 accuracy of all HCP tiers in rare diseases using the device in the study - Improvement in Top-1 sensitivity of all HCP tiers in rare diseases - Top-1 sensitivity of all HCP tiers in rare diseases using the device in the study - Improvement in Top-1 specificity of all HCP tiers in rare diseases - Top-1 specificity of all HCP tiers in rare diseases using the device in the study - improvement in Top-1 accuracy of the primary care HCP in rare diseases - Top-1 accuracy of the primary care HCP in rare diseases using the device - Improvement in Top-1 sensitivity of the primary care HCP in rare diseases - Top-1 sensitivity of the primary care HCP in rare diseases using the device - Improvement in Top-1 specificity of the primary care HCP in rare diseases - Top-1 specificity of the primary care HCP in rare diseases using the device - Improvement in Top-1 accuracy of the dermatologists in rare diseases - Top-1 accuracy of the dermatologists in rare diseases - Improvement in Top-1 sensitivity of the dermatologists in rare diseases - Top-1 sensitivity of the dermatologists in rare diseases using the device - improvement in Top-1 specificity of dermatologists in rare diseases - Top-1 specificity of dermatologists in rare diseases using the device	- An increase of at least 26% in the diagnostic accuracy of rare dermatological conditions. - A diagnostic accuracy with the assistance of the device for rare diseases of at least 54% across all HCP tiers. - An increase of at least 21% in the diagnostic sensitivity of rare dermatological conditions. - A diagnostic sensitivity with the assistance of the device for rare diseases of at least 41% across all HCP tiers. - An increase of at least 22% in the diagnostic specificity of rare dermatological conditions. - A diagnostic specificity with the assistance of the device for rare diseases of at least 60% across all HCP tiers. - An increase of at least 30% in the diagnostic accuracy of rare dermatological conditions in primary care. - A diagnostic accuracy with the assistance of the device for rare diseases of at least 52% in primary care. - An increase of at least 25% in the diagnostic sensitivity of rare dermatological conditions in primary care. - A diagnostic sensitivity with the assistance of the device for rare diseases of at least 44% in primary care. - An increase of at least 23% in the diagnostic specificity of rare dermatological conditions in primary care. - A diagnostic specificity with the assistance of the device for rare diseases of at least 62% in primary care. - An increase of at least 13% in the diagnostic accuracy of rare dermatological conditions in dermatology. - A diagnostic accuracy with the assistance of the device for rare diseases of at least 61% in dermatology. - An increase of at least 16% in the diagnostic sensitivity of rare dermatological conditions in dermatology. - A diagnostic sensitivity with the assistance of the device for rare diseases of at least 52% in dermatology. - An increase of at least 15% in the diagnostic specificity of rare dermatological conditions in dermatology. - A diagnostic specificity with the assistance of the device for rare diseases of at least 71% in dermatology.
5RB	The device measures the degree of involvement of disease objectively, quantitatively, and reproducibly. This increases the precision of healthcare providers during the monitoring of patients. This has a positive impact on patient management and outcomes related to the monitoring of patients and treatment.	- Inter-observer Intraclass correlation coefficient - Intra-class Intraobserver correlation variability - Experts considered the device a positive tool for increasing objectivity in patient monitoring through the Clinical Utility Questionnaire - Specialists express a preference for an app to identify the severity of cases - Correlation assessing androgenetic alopecia severity between the device and HCPs - Unweighted Kappa assessing androgenetic alopecia severity	- A 72.70% ICC with the gold standard assessing HS - Less than 10% of variability in HS severity assessment between consecutive visits. - At least a 65% of correlation assessing androgenetic alopecia - 60% unweighted kappa assessing androgenetic alopecia severity. - 80% of experts express a preference for an app to identify the severity of cases
0ZC	The device improves accuracy of HCPs during the remote diagnosis of skin conditions and the precision of HCPs during the remote referral. This has a positive impact on patient management and outcomes related to diagnosis and monitoring of patients, especially during remote care.	- Number of patients that can be handled remotely - Sensitivity to identify necessary referrals in teledermatology - Specificity to identify unnecessary referrals in teledermatology - Experts considered the device a useful tool to gather more patient information - Experts perceive the use of the device for diagnostic support as very useful	- 58% of patients can be handled remotely with the assistance of the device. - A sensitivity of at least 30% in identifying necessary referrals remotely. - A specificity of at least 65% in identifying unnecessary referrals remotely. - At least 70% of experts consider the device a useful tool to collect patient data during teleconsultations. - At least 70% of experts perceive the device as a useful tool to handle remotely most of patients.

7GH: This benefit will be assessed in the clinical validations of the device and compared to the state of the art. For this purpose, the diagnostic accuracy of the HCPs will be assessed aided and unaided by the device.
3KX: This benefit will be assessed by determining the reduction of cumulative waiting time with the use of the device by the HCPs, since Spain is one of the regions with the longest waiting time to see a dermatologist, 131 days. Furthermore, they will be asked whether the use of the device enables them to reduce the consultation time.
8PL: This benefit will be assessed based on the suitability and number of referrals deemed necessary with and without the assistance of the device by primary care practitioners.
1QF: This benefit will be assessed in the clinical validations of the device and compared with the current state of the art. In this way, the capacity of the device of detecting malignancy will be assessed.
9VW: This benefit will be assessed in the clinical validations of the device. Thus, the diagnostic accuracy of HCPs of rare dermatological diseases will be assessed with and without the use of the device.
5RB: This benefit will be assessed in the clinical validations of the device. It will be measured whether the device can measure the severity of dermatological conditions in the same way as an expert dermatologist.
0ZC: This benefit will be verified in the clinical validations of the device. It will evaluate the capacity of the device to handle remotely patients as well as the perception of the experts of the utility of the device being used in teleconsultations.

Device classification

Legit.Health Plus is classified as a Class IIb device according to Rule 11 of Annex VIII of MDR 2017/745.

Software intended to provide information which is used to take decisions with diagnosis or therapeutic purposes is classified as class IIa, except if such decisions have an impact that may cause:

death or an irreversible deterioration of a person's state of health, in which case it is in class III; or
a serious deterioration of a person's state of health or a surgical intervention, in which case it is classified as class IIb.

Software intended to monitor physiological processes is classified as class IIa, except if it is intended for monitoring of vital physiological parameters, where the nature of variations of those parameters is such that it could result in immediate danger to the patient, in which case it is classified as class IIb. All other software is classified as class I.

The software in question is classified as Class IIb for its application in melanoma detection based on its intended use and the potential impact on patient health. This significant potential for patient harm from a misdiagnosis justifies classifying the software as Class IIb.

Product category

Software-only medical device.

Device variants and packaging

No variants are available for the device.

Previous version of the device

The predecessor of the current device is named Legit.Health (hereinafter, "the legacy device"). 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.

The legacy device has been commercialized since 2020 (after obtaining the manufacturing license in Spain) and was certified under the Medical Devices Directive (MDD).

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.

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.

Device lifecycle

The device is not yet CE marked and has not been commercialized yet. Although it has a previous version as a legacy device, which has been in the market since 2020 and is CE marked under MDD European Regulation as a device class I.

Expected lifetime

The expected operational lifetime of the device is established at 5 years, which is subject to regular software updates and the lifecycle of the integrated components and platforms. The lifetime will be increase in equivalent spans as the design and development continues and maintenance and re-design activities are carried out.

This timeline accounts for the expected evolution of the underlying operating systems and tools, the progression of medical device technology, and the necessary update cycles to maintain security and operability.

Degree of Novelty

Clinical or Surgical Procedure Novelty Dimensions
	Is there novelty? Yes/No	If Yes – Specifically describe novel features and any potential clinical or health impact. If No – Provide evidence/justification to demonstrate non-novel features
Mode of use or Treatment option	Yes	The novelty in the Mode of Use is that the assessment of skin lesions is performed using a photograph analyzed by an AI-powered device, rather than solely by visual inspection. Additionally, it enables the primary care practitioner to assess skin conditions with higher accuracy. This changes how the diagnostic assessment is conducted. This new mode of use enables a novelty in the Treatment/Management Option. By providing a reliable, non-invasive analysis for benign pathologies, the device introduces a clinical pathway to enable the option to avoid a biopsy, replacing an invasive procedure with a non-invasive one.
Device-Patient Interface	No	Not novel - No direct interface with the patient. The interface is through digital image capture, as in common practice. Additionally, the patient is not the intended user of the device.
Interaction and Control	Yes	The novelty in "Interaction and Control" lies in shifting the diagnostic process from a purely human assessment to an interaction between the practitioner and an artificial intelligence analysis. This complete workflow creates a new mode of use for dermatological diagnosis, which is particularly useful in remote or primary care settings.
Deployment Methods	No	The software is deployed on standard platforms (e.g., mobile devices, web) and integrated into typical clinical workflows.
Clinical Workflow	Yes	The novelty in the “Clinical workflow” is that the device assists the practitioners in the decision-making process. By processing skin images, the device provides the physician with additional clinical information that allows them to make a diagnosis more quickly and decide whether to refer the patient to specialized care or, alternatively, monitor the patient in primary care, thereby reducing workload and waiting lists.
Device Related Novelty Dimensions
Medical Purpose	Yes	The primary medical purpose of the device is to assist practitioners in the diagnosis and severity assessment of a range of dermatological conditions. However, a novel medical purpose is established by its specific application to addressing previously unmet medical needs. The device is designed and validated to aid in the diagnosis of rare dermatological conditions (such as Generalised Pustular Psoriasis, Pemphigus Vulgaris or Palmoplantar pustulosis). In this context, where reliable and objective diagnostic tools are scarce, applying this technology constitutes a novel medical purpose.
Design	Yes	The novelty in the “Design” of the device lies in its algorithms, which have been trained to allow physicians to obtain additional information about the suspected diagnosis, the severity of the disease, and whether or not to prioritize a patient for referral.
Mechanism of Action	No	Not novel - The device uses AI for image analysis and quantification, but these are established methods in dermatological software.
Materials	No	Not applicable - Software only.
Site of Application	No	Not novel - The software analyzes dermatological images; no direct contact or application to the patient.
Components	Yes	The novelty in the “Components” of the device is its proprietary artificial intelligence (AI) algorithm. This software component is integral and necessary for the device's function, performing the analysis of clinical images to quantify the severity of the skin condition. The novelty of this component lies in its unique architecture and the fact that it has been custom-trained on a curated dataset of dermatological images to achieve the intended clinical performance for its specific medical purpose. While the device operates on non-novel hardware (a standard smartphone), the algorithm itself constitutes the core innovative component.
Manufacturing Process	No	Not novel - Developed using standard software development and validation processes, including lifecycle and risk management.
Novelty Conclusion	From a clinical perspective, the device introduces moderate novelties and moderate clinical impact in dermatological practice, as it: _ The device offers a new methodology to assess skin conditions, rather than solely use visual inspection, it enables HCPs to have additional clinical information, improving diagnostic accuracy and the decision-making process, therefore it improves the clinical workflow. _ The device is made up of novel algorithms that allow it to address the needs of physicians that were previously unmet in clinical practice and offers a new tool to obtain information. On the other hand, it does not introduce new treatment or diagnostic approaches and does not create a new category of medical intervention. Thus, the device provides practitioners with a new tool for assessing skin conditions, in addition to those currently available in clinical practice (visual examination, use of a dermatoscope, or invasive procedures such as biopsies), improving decision-making and so that clinical workflow. However, the device's innovation is moderate, as it does not modify any standard clinical practice procedures in dermatology or offer new treatments. Furthermore, the algorithms it uses meet the needs of professionals, but their architecture and the way dermatological images are captured for processing are procedures used in medical devices used in dermatology.

Clinical Performance Claims

In order to assess compliance with specific requirements on performances (GSPR 1), the clinical evaluation report will notably have to assess whether the device under evaluation achieves the performance intended by its manufacturer.

In that purpose, the document Performance claimsof the device provides a detailed description of its clinical performance, with the used means of measure and the claimed acceptance criteria. this clinical evaluation plan provides below a detailed description of intended clinical performances, with the used means of measure and the claimed acceptance criteria.

For more information in relation to performance claims of the device, please check the document Performance claims.

Risk management

Risk management was performed as part of the development process. The risk assessment was conducted in accordance with the 2017/745 MDR and the international standard ISO 14971:2019 Medical Devices-Application of Risk Management to Medical Devices.

Risk management and clinical evaluation are interlinked at many levels: The clinical evaluation shall consider data from risk management activities related to our device as input data for defining relevant safety parameters. Also, any unacceptable residual risk from the risk analysis must be specifically assessed in the CER to provide supporting evidence that the clinical benefits outweighing the residual risk. Finally, clinical evaluation and its periodic reviews and updates is a relevant source of input data for the maintenance and corresponding review and update of the product's risk analysis

| Standard | Title | | ----------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | --- | | EN ISO 13485:2016/A11:2021 | Medical devices. Quality management systems. Requirements for regulatory purposes | | EN ISO 15223-1:2021 | Medical devices. Symbols to be used with medical device labels, labeling and information to be supplied. Part 1: General requirements. | | EN ISO 20417: 2021 | Medical devices - Information to be supplied by the manufacturer. | | EN ISO 14971:2019 | Medical devices. Application of risk management to medical devices. | | EN ISO 20417:2021 | Medical devices - Information to be supplied by the manufacturer | | UNE-EN 62304:2007/A1:2016 (EN 62304:2006/A1:2015) | Medical device software - Software life-cycle processes | | UNE-EN 62366-1:2015/A1:2020 (EN 62366-1:2015/A1:2020) | Medical devices - Part 1: Application of usability engineering to medical devices | | IEC 82304-1:2016 | Health software - Part 1: General requirements for product safety | |

Generic hazards applicable to the device

According to the risk management, the following generic hazards are considered relevant to address the safety of the device:

Incompatibility with other devices
Inadequate labelling
Inadequate instructions for use
Inadequate specification of accessories
Complicated instructions for use
Instructions for use not available or separate from the product
Use by untrained personnel
Reasonably foreseeable misuse
Inadequate warning of adverse effects
Incorrect diagnosis
Wrong data transfer
Misinterpretation of results
Incompatibility with consumables, accessories or other devices
Inadequate to the planned functions
Inadequate or absent maintenance specifications, including performance checks
Inadequate maintenance
Absence of limitation of product lifetime

The clinical evaluation for the product shall address all these points through assessing the relevant clinical evidence, determining if there is enough evidence to support a good safety profile.

Risk mitigation measures

We believe that the mitigation controls, will be sufficient to provide reasonable assurance of safety and efficacy for the device. The information below summarizes the potential risks identified for the subject device with respect to its intended purpose.

The risk assessment of the hazards of the device and the design, process and application of the product has been performed by us. The complete table of risks and risk control measures can be found in the R-TF-013-002 Risk Management Record.

All residual risks have been reduced as far as possible. All individual and residual risks were determined to be acceptable according to the risk acceptability criteria described in the R-TF-013-003 Risk Management Report.

The risks identified in the risk management file prior to the clinical evaluation, which have already been mitigated to the maximum extent possible, will be monitored during the clinical studies to verify the effectiveness of the mitigation measures and identify any deviations from the identified risks. Upon completion of the clinical evaluation, the final column of the risk matrix will be completed to confirm the "Verification of effectiveness of risk control measures". All safety findings identified during the clinical evaluation will be investigated.

Risk ID	Hazard	Mitigation measures
R-75H	Incorrect clinical information	Information about device outputs and intended user (HCP) are detailed in the IFU. The medical device returns metadata about the output that helps supervising it, such as explainability media and other metrics.
R-DAG	Incorrect diagnosis or follow up	Information about device outputs and intended user (HCP) are detailed in the IFU. The medical device returns metadata about the output that helps supervising it, such as explainability media and other metrics The device returns an interpretative distribution representation of possible ICD categories, not just one single condition.
R-AGQ	Image artefacts/resolution	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. The device returns meaningful messages to the users about the quality score of the images, this allows care providers to re-take a photo. We also offer training to the users to optimize the imaging process so that it is optimal for the device's operation.
R-T8Q	Data transmission failure from healthcare provider's system	State-of-the-art techniques of security and software availability. The device returns meaningful messages about the error to help troubleshooting.
R-3N5	Data input failure	State-of-the-art techniques of security and software availability. The device returns meaningful messages about the error to help troubleshooting.
R-YF4	Data accessibility failure	State-of-the-art techniques of security and software availability. The device returns meaningful messages about the error to help troubleshooting.
R-LRP	Data transmission failure	State-of-the-art techniques of security and software availability. The device returns meaningful messages about the error to help troubleshooting. The endpoints of the device follow HL7's FHIR interoperability standard.
R-5L4	Inadequate lighting conditions during image capture	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. The device returns meaningful messages to the users about the quality score of the images, this allows care providers to re-take a photo. We also offer training to the users to optimize the imaging process so that it is optimal for the device's operation.

Safety endpoints

In accordance with Section 1.a of Annex XIV of Regulation (EU) 2017/745 on medical devices, this Clinical Evaluation Plan specifies the methods to examine both qualitative and quantitative aspects of clinical safety, with particular focus on the identification and assessment of residual risks and potential side-effects. The subsequent Clinical Evaluation will summarize complication and adverse event rates generated during device validation, and—taking into account measurement uncertainties and clinical variability—may recommend updates to the Risk Management File or the introduction of corrective and preventive actions, which will be documented in both the final Clinical Evaluation Report and the post-market surveillance process. All residual risks recorded in the Risk Management File (R-TF-013-002) will be reviewed from a clinical perspective, since any such risk could harm patients or users and must therefore be addressed during the device's clinical validation activities. During the clinical evaluation, the verification and validation of the effectiveness of the risk control measures identified in the risk management file will be confirmed under actual conditions of clinical use, as required by Annex XIV, Part A, Section 1(d) of Regulation (EU) 2017/745 (MDR), and in line with the guidance provided in MDCG 2020-6. From these residual risks we have derived specific safety objectives, each aligned with identified residual risk, ISO 14971:2019 (Clauses 7.3-7.4 and 8), the General Safety and Performance Requirements of MDR Annex I, and the clinical evidence provisions of Article 61 of the MDR.

Safety objective	Risk ID	Identified Residual Risk	Potential harm to the patient	Means of measure	Endpoint/Acceptance criteria
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	R-75H	The care provider receives into their system data that is erroneous.	Misdiagnosis or delayed diagnosis or inadequate prioritisation, leading to inappropriate or unnecessary treatment, progression of the underlying condition, or adverse events from incorrect therapy.	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%).
Demonstrate that the frequency of device-related diagnostic errors and their downstream clinical consequences are lower than that defined in its intended use	R-DAG	The medical device outputs a wrong result	Misdiagnosis or delay in diagnosis, prioritisation or inappropriate clinical management (e.g. unnecessary tests or treatments) and worsening patient's health status.	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%).
Image acquisition without interferences or artifacts	R-AGQ	The medical device receives an input that does not have sufficient quality in a way that affects its performance	Delay in consultations to take an image with an acceptable quality. Misdiagnosis, delays in treatment	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%).
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.	R-T8Q, R-3N5, R-YF4 and R-LRP	Failure of interoperability between the medical device and the healthcare provider's system, resulting in an inability to establish a connection or perform bidirectional data exchange.	Delayed or missed diagnostic support leading to postponed or inappropriate clinical decisions, potentially worsening patient outcomes.	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%).
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.	R-5L4	The medical device receives an input that does not have sufficient quality	Interferences in the device's performance that can lead to misdiagnosis, delays in proper treatment and worsening of the patient's health status.	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%).

These measurable endpoints ensure that safety is rigorously assessed and comparable to or exceeding the state of the art.

While these risks are mitigated through technical and procedural controls, Post-Market Surveillance (PMS) will monitor any potential occurrences post-market.

To note that for the next clinical evaluation plan, this assessment will be completed through monitoring and assessment of the complication rates collected during PMS and PMCF activities, using the theoretical residual probability of occurrence defined in the Risk Management File (RMF) (x occurrence per patient).

By mapping each safety objective directly to the regulatory and standardised requirements, we ensure full compliance and robust justification for the clinical validation of residual risk controls.

Acceptability of the benefit-risk ratio

The benefit/risk ratio for the device shall be deemed acceptable as long as:

All the applicable safety-related GSPRs are met based on a critical assessment of the relevant safety parameters defined in the previous section providing a good safety profile for the device;
All the performance-related GSPRs are met, based on the outcome parameters defined in the previous Section and as assessed through a critical revision of relevant clinical evidence.
Any residual risks with clinical impact as identified in the risk assessment of the device are adequately addressed.
All the clinical benefits are fulfilled and their benefits are superior to the residual risks identified in the risk assessment.
PMS and PMCF activities are planned to keep monitoring and assessing the risks and side-effects once the device is on the market.

The current Clinical Evaluation aims to confirm the benefits of the device and ensure its safety through the review of the Post-market Surveillance Plan and Risk Management documentation.

State of the art

Scope

This state-of-the-art document is established in the framework of the clinical evaluation of the Legit.Health Plus medical device. Therefore, it aims to specify the clinical background and current knowledge and to establish the state of the art for the current clinical practice and medical devices used in dermatology.

The current state of the art in the corresponding medical field, the following aspects and information will be checked:

Applicable standards and guidance documents.
Information relating to the current situation in the medical field in which the device is used.
Benchmark devices and other devices available on the market.

The literature search must be based on a literature search protocol.

Literature search protocol

The literature search protocol used for the review of the state of the art, as well as the results of this literature review is presented in the separate document R-TF-015-011 State of the Art Legit.Health Plus.

This separate document was created to avoid duplication as the current knowledge/state of the art should be present in both the clinical evaluation plan and report.

Thus, this section aimed to present the literature search protocol used for the identification of clinical data on the device under evaluation and the legacy device.

Literature search

Literature search protocol

In accordance with the section A5 of the MEDDEV 2.7/1 rev4 guide, the expression of the objective of the literature search will use the PICO methodology (Problem/patient/population, type of Intervention, Comparator, and relevant Outcomes).

The selection of relevant articles from references identified on the databases is based on the description of the research objective presented in the following sections.

	Inclusion	Exclusion
Patient	Patients with visible skin structure abnormalities; skin diseases listed in ICD-11 code 14; across all age groups, skin types, and demographics. Users: Healthcare Professionals (HCPs) such as dermatologists, General Practitioners (GPs) and IT professionals.	Wrong type of population: - Animals. - Studies focused on non-dermatological pathologies
Intervention/indicator	Use of a computational software-only medical device (SaMD) that processes images of skin structures to provide clinical data for aiding practitioners in skin assessments.	Interventions not related to the device's intended use or medical indication.
Comparator and type of studies	Other smartphone applications. SkinVision, Molescope, Huvy and DERM. Traditional methods of clinical skin examination without software assistance, and non-software-based skin assessments by healthcare professionals (i.e., Standard of Care). Type of studies: - Meta-analysis - Literature review and systematic reviews - Case series and cohort studies - Clinical studies (randomised or not, multicentric or not, prospective or retrospective). Clinical guidelines or guidelines elaborated by scientific societies.	Wrong comparator and studies: - Non-clinical comparators (e.g., comparison against another algorithm only). - Purely in silico or in vitro validation studies without clinical practice data. - Case reports that do not provide new information on risks or performance. - Non-peer-reviewed literature (e.g., opinion articles, blog posts). - Study providing no clinical results (e.g. protocols)
Outcomes	Improved efficiency and accuracy in clinical decision-making for skin disease assessment or malignancy detection; support in diagnosis through interpretative data and quantification. Optimisation of clinical workflow through reduction of unnecessary referrals from primary care to dermatology; reduction of cumulative waiting time to see the dermatologist face-to-face. Safety data (e.g. incorrect performance, failure of interoperability, inputs without sufficient quality).	Wrong objectives: Not clinical outcomes (e.g., technical algorithm testing) Datasets not discussing the correct use, safety, performance, or benefits of the device Data only focused on drugs are excluded Too specific topic (i.e. datasets dealing with a particular subject and deemed irrelevant for the description of the state of the art)

Source of data and search description

As per MEDDEV 2.7/1 rev.4 guidance document, a comprehensive search strategy normally involves multiple scientific databases. The following sources of data will be searched for this clinical evaluation:

The three following databases will be used for literature searches seeking clinical data relating to the device under evaluation:

MEDLINE PubMed will be consulted to identify pertinent data related to the device under evaluation. This free access database which comprises over 30 million citations for biomedical articles from MEDLINE, PMC, life science journals and online books, is the most pertinent database for biomedical searches.
Google Scholar provides comprehensive coverage of clinical research publications, including those from European journals, and searches the body and text of articles
for search terms. The comprehensive coverage of Google Scholar has been confirmed in peer-reviewed research.
ClinicalTrials.gov is an online clinical trials registry maintained by the United States National Library of Medicine on behalf of the National Institutes of Health (NIH), medical research institutes in the United States. As of July 15, 2025, the registry boasts of having published information on more than 503,000 clinical trials that have been conducted in 220 countries.

As per MEDDEV 2.7/1 rev.4, literature found to be relevant is likely to cite other literature that is of direct interest for the clinical evaluation report. In such case, the publications deemed of interest will be searched and appraised as the other scientific articles.

Following the acceptance criteria described in the Table above, the following search have been conducted by Mr. Jordi Barrachina on July 15th for the device under evaluation.

ID	*Database*	*Keywords/query*	*Filter*	*Records*
01	PubMed	“Legit.Health”	No limitations	10
02	Google Scholar	"Legit.Health" and "AI Labs Group"	No limitations	3
03	ClinicalTrials.gov	“Legit.Health”	No limitations	2

Duplicates were identified using the unique references of the article (PMID, DOI and Pudmed Identifier). For publications that have no unique identifier, duplicates were identified using mainly the title, the authors and the source of the document.

Articles can also be added manually if they are deemed relevant and consistent with the research objectives. These publications can be identified within the selected articles.

Vigilance databases

Not applicable as this clinical evaluation plan is done for Legit.Health Plus and CE-mark submission (1st commercialisation) so it has not still been commercialized.

Registres

During the analysis of the registry reports for the state of the art presentation, specific clinical data relating to the device under evaluation was also sought.

Selection Methodology and Criteria

A systematic and objective search and review protocol will be implemented to identify relevant clinical data. While this search is designed to be comprehensive, it is acknowledged that some pertinent studies may be inadvertently omitted due to limitations such as search language or publication indexing.

The screening process will be conducted in three sequential stages:

An initial review of article titles.
A secondary review of abstracts.
A final full-text assessment of the materials and methods.

At each stage, publications will be included or excluded based on the predefined PICO criteria (detailed in section Literature Search Protocol). The complete selection process will be documented within the clinical evaluation report to ensure full traceability and reproducibility.

Literature appraisal data

Appraisal plan

The relevance and weight of all clinical data will be critically assessed to determine their contribution to the evaluation of the device's clinical performance and safety. This appraisal will encompass data sourced from the systematic literature review as well as clinical data generated and held by the manufacturer (see Section 11 Clinical Evidence). The entire process will be conducted in adherence to the manufacturer's pre-established appraisal plan and the methodology outlined in the MEDDEV 2.7/1 rev. 4 guidance.

Appraisal and weighting criteria

As exposed in MEDDEV 2.7/1 Rev. 4, uncertainty arises from two sources: the methodological quality of the data, and the relevance of the data to the evaluation. Consequently, datasets identified and selected in previous sections have been appraised using the criteria exposed in the table below. These criteria are based on the the IMDRF MDCE WG/N56FINAL:2019 (formerly GHTF/SG5/N2R8:2007) – Clinical Evaluation.

ID	Criteria	Description	Grading System	Criteria	Score
CRIT1	Study Focus	Do the data relate to a relevant clinical alternative?	Direct Relevance	Data on a similar device (e.g., devices tagged as similar) OR on the standard clinical practice (e.g., accuracy of HCPs, visual inspection in Primary Care).	2
CRIT1	Study Focus	Do the data relate to a relevant clinical alternative?	Contextual Relevance	Contextual data (e.g., disease epidemiology, general clinical guidelines) but not on the performance of a specific alternative OR Clinical data including a similar device but which is not specific	1
CRIT1	Study Focus	Do the data relate to a relevant clinical alternative?	No Relevance	Data not related to any clinical alternative in dermatology	0
CRIT2	Clinical Setting or Intended use	Does the study's setting and intended use match the device under evaluation?	Full match	Data focused on devices designed to support healthcare practitioners in the assessment of skin structures OR Same setting (e.g., Primary Care and/or Dermatology clinic).	2
CRIT2	Clinical Setting or Intended use	Does the study's setting and intended use match the device under evaluation?	Partial match	Data focused on devices with an intended use not claimed by the manufacturer, but compliant with the intended use of the device group OR Same setting but for a different intended use (e.g., melanoma detection only).	1
CRIT2	Clinical Setting or Intended use	Does the study's setting and intended use match the device under evaluation?	No match	Data focused on devices with an intended use not related to the device under evaluation OR Different clinical setting (e.g., specialities different from dermatology).	0
CRIT3	Population of patients	Is the study population representative?	Applicable	Target population as per the device's intended use (e.g., patients attending a dermatological consultation across all age groups, skin types, and demographics)	2
CRIT3	Population of patients	Is the study population representative?	Partially applicable	Specific sub-population of the target population (e.g., only high-risk patients, only a specific skin phototype, only a pathology).	1
CRIT3	Population of patients	Is the study population representative?	Not applicable	Population not related to the target population (e.g., healthy volunteers) or non-relevant or contraindicated population.	0
CRIT4	Type of dataset	Appropriate study design/type of document and sufficient data	Yes	Studies with a level of evidence greater than or equal to 4 (as per Level of Evidence scale)	1
CRIT4	Type of dataset	Appropriate study design/type of document and sufficient data	No	Studies with a level of evidence lower than 4 (e.g., expert opinions, small case series). OR insufficient data to extract relevant clinical performance or safety information.	0
CRIT5	Outcome measurement (Performance/Safety)	Does the study measure objective outcomes related to performance (e.g., diagnostic accuracy) and/or safety (e.g., false negative rate)?	Yes	Provides quantitative performance data (e.g., Sensitivity, Specificity, PPV) and/or safety data (e.g., rate of unnecessary biopsies, false negatives).	1
CRIT5	Outcome measurement (Performance/Safety)	Does the study measure objective outcomes related to performance (e.g., diagnostic accuracy) and/or safety (e.g., false negative rate)?	No	Does not provide performance or safety data (e.g., descriptive only).	0
CRIT6	Clinical significance	Does the study evaluate if the performance results in a tangible clinical benefit (e.g., reduction in unnecessary biopsies, improved early detection)?	Yes	Provides clinical benefit data (e.g., impact on referral pathways, reduction of benign biopsies) or workflow benefits.	1
CRIT6	Clinical significance	Does the study measure clinical significance (e.g., impact on patient management, health outcomes)?	No	Does not provide clinical benefit data (reports pure performance metrics only or descriptive).	0
CRIT7	Statistical analysis	Is there a statistical analysis?	Yes	tatistical comparisons are made (e.g., between groups, p-values, confidence intervals).	1
CRIT7	Statistical analysis	Is there a statistical analysis?	No	No statistical comparison (descriptive data only).	0

All included datasets are appraised for their relevant methodological quality and scientific validity (from 0 to 4) and clinical relevance (from 0 to 6). The weight of each data set is measured by the score calculated from the sum obtained (from 0 to 10). Articles with a score higher of 4 will be evaluated in the R-TF-015-011 State of the Art Legit.Health Plus and the results of the appraisal will be also documented in this document. If the score of a data set is < 4, a justification for the use of the data set is included.

Clinical Development Plan

The Clinical Development Plan (CDP) indicates progression from exploratory investigations, such as first-in-man studies and pilot studies, to confirmatory investigations, such as pivotal clinical investigations and a PMCF with an indication of milestones and a description of potential acceptance criteria.

Purpose

To establish the roadmap of clinical evidence required for the device, ensuring compliance with the General Safety and Performance Requirements (GSPR).

Current State of the Evidence

Concise summary of available evidence justifying the initiation of the CDP phases.

Non-clinical Test results - bench testing

The technical performance of the device aims to demonstrate that the MDSW's ability to accurately, reliably and precisely generate the intended output (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)), from the input data (images of visible skin structure abnormalities).

The clinical evaluation will take into consideration the relevant pre-clinical and performance data listed below.

Software Design Verification

Design Verification and Validation confirm the device's compliance with Design Requirements under real and simulated conditions in accordance with the principles of IEC 62304:2006 + Amd 1:2015 (Medical device software — Software life cycle processes) and IEC 82304-1:2016 (Health software — Part 1: General requirements for product safety). The complete results and traceability of all Verification activities are documented in the Report, reference R-TF-012-038.

AI Model Validation and Testing

The Artificial Intelligence (AI) model, which constitutes the core component of the device, has undergone a dedicated validation and testing process to ensure its accuracy, robustness, reliability, and cybersecurity in line with its intended medical purpose.

This validation was performed in alignment with state-of-the-art principles and the conformity assessment requirements for high-risk AI systems as described in the Regulation (EU) 2024/1689 (Artificial Intelligence Act). The process adheres to the software lifecycle requirements of IEC 62304:2006 + Amd 1:2015 and is further guided by the principles of Good Machine Learning Practice (GMLP).

The validation process included a rigorous assessment of the training, validation, and testing datasets; performance testing on an independent (held-out) test dataset to evaluate key metrics (e.g., sensitivity, specificity, accuracy); and an analysis of the model's generalisability and robustness against potential biases. The complete AI Model Validation and Testing protocol, results, and traceability are documented in the R-TF-028-005 AI Development Report.

Usability Engineering

The formative evaluation of the device's usability has been completed in accordance with the principles of IEC 62366-1:2015 (Application of usability engineering to medical devices) and is documented in the Formative Evaluation Report (Ref: R-TF-012-008 Formative Evaluation Report_2023_001).

The summative usability validation will be conducted as an integral part of the upcoming clinical investigation. This will serve to confirm the safety and effectiveness of the user interface when used by intended users in a representative clinical environment.

The evidence gathered from this complete usability engineering process will be incorporated into the Clinical Evaluation Report. This data will be used to demonstrate that risks associated with use error have been minimized and that the device can be used safely and effectively by the intended users, thereby supporting compliance with the relevant General Safety and Performance Requirements (GSPRs).

Existing clinical data

The systematic literature review, documented in the State of the Art report (R-TF-015-011 State of the Art Legit.Health Plus), concluded that while existing data supports the clinical background and the general state of the art, it provides insufficient direct evidence to fully confirm the clinical performance, safety, and benefits of Legit.Health Plus itself. A gap was identified in demonstrating the device's performance in a real-world clinical environment with its intended user population.

To address this evidence gap and to generate the necessary clinical data, pivotal investigations were designed and conducted. The primary objective of these studies was to definitively evaluate the device's clinical safety and performance and to provide robust evidence supporting its intended clinical benefits, thereby demonstrating conformity with the relevant General Safety and Performance Requirements (GSPRs).

Accordingly, the following pivotal investigations have been conducted:

Confirmatory phase (Pivotal Investigations)

To demonstrate compliance with the GSPR regarding clinical performance, safety, and clinical benefit, with statistical significance if applicable.

Study Identification	Study type	Main objectives	Milestones	Acceptance Criteria
Reference of the protocol: AIHS4	Type of study: Pivotal study State of process: Completed Study design: Retrospective, observational, longitudinal and pivotal study	Primary objective: To evaluate the accuracy and reliability of the AIHS4 system, integrated into the device, by comparing it with clinical experts using IHS4 and a gold standard in the context of the phase 1 clinical trial M-27134-01 for Hidradenitis Suppurativa (HS). Secondary objectives: - To compare AIHS4 performance with interobserver agreement levels reported in the literature. - To assess temporal variability in AIHS4 scoring across consecutive visits. - To analyse AIHS4 performance by anatomical region.	Inclusion period: June 4, 2024 to July 11, 2024 This study included 2 patients with 16 severity assessments. Completion date: July 11, 2024 Date of the study report: February 28, 2025.	- AIHS4 improves severity assessment compared to interobserver agreement. - AIHS4 maintains temporal consistency across visits, and temporal variability is acceptable ≤15%. - AIHS4 achieves high agreement in lesion classification across anatomical regions, ICC=70%.
Reference of the protocol: BI 2024	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, cross-sectional and pivotal study.	Primary objective: Validate that the device improves diagnostic accuracy for generalised pustular psoriasis (GPP). Secondary objectives: Validate that the information provided by the device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of other dermatological skin conditions, such as hidradenitis suppurativa.	Initiation date: June 1, 2024 Inclusion period: June 1, 2024 to September 15, 2024 This study included 15 practitioners (4 dermatologists and 11 PCPs) to assess 100 images of skin conditions. Completion date: September 15, 2024 Date of the study report: September 15, 2024	- An improvement of at least 10% in diagnostic accuracy for generalised pustular psoriasis (GPP) when used by primary care physicians, and at least 5% when used by dermatologists, compared to standard clinical practice. - An improvement of at least 10% in diagnostic accuracy for skin conditions when used by general practitioners, and at least 5% when used by dermatologists, compared to standard clinical practice. - An improvement of at least 14% in diagnostic sensitivity for skin conditions when used by general practitioners, and at least 7% when used by dermatologists, compared to standard clinical practice. - An improvement of at least 11% in diagnostic specificty for skin conditions when used by general practitioners, and at least 10% when used by dermatologists, compared to standard clinical practice. - An improvement in diagnostic accuracy, sensitivity and specificity of both primary care and dermatologists in the diagnosis of rare dermatological conditions.
Reference of the protocol: COVIDX EVCDAO 2022	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, cross-sectional and pivotal study.	Primary objective: To ascertain the validity of the device, leveraging artificial intelligence developed by AI Labs Group S.L., in objectively and reliably tracking the progression of chronic dermatological conditions. This validation is deemed successful if the tool achieves a score of 8 or higher on the Clinical Utility Questionnaire (CUS). Secondary objectives: - Confirming that the utilization of the device elicits a high level of patient satisfaction, particularly in its remote application. - Demonstrating that the implementation of the device leads to a reduction in face-to-face consultations, thereby optimizing healthcare resources and patient convenience. - Validating the device's ability to consistently generate reliable condition monitoring, thereby establishing its trustworthiness as a monitoring system.	Initiation date: April 13, 2022 Inclusion period: April 13, 2022 to October 23, 2023 This study included 5 practitioners to assess 160 patients with different skin conditions. Completion date: October 23, 2023 Date of the study report: October 17, 2023.	- A score of 8 or higher in the Clinical Utility Score (CUS) is filled by the medical staff. - At least 75% of experts (dermatologists) must answer positively to the questions of Clinical Utility Questionnaire (CUS), Data Utility Questionnaire (DUQ), and Usability Questionnaire (SUS) (Experts' consensus criteria).
Reference of the protocol: DAO Derivación O 2022	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, longitudinal and pivotal study.	Primary objective: To validate that the device is a valid tool for improving the adequacy of referrals to dermatology. Secondary objectives: - To validate that the device reduces costs in secondary care. - To validate that the device reduces dermatology waiting lists. - To validate that the device optimizes clinical flow in Osakidetza.	Initiation date: November 23, 2022 Inclusion period: November 23, 2022 to May 6, 2025 This study included 127 patients with different skin conditions. Completion date: May 6, 2025 Date of the study report: May 22, 2025.	- Improve the adequacy of referrals to dermatology. - A reduction of unnecessary referrals to dermatology (at least 15%). - A sensitivity and a specificity equal to or superior to the primary care physician to identify necessary referrals. - A sensitivity and a specificity equal to or superior to the primary care physician to identify necessary referrals in teledermatology. - A reduction of waiting lists (at least 30% Warshaw et al. 2011). - A reduction of the costs in secondary care. - An AUC (Area Under the Curve) of at least 0.8 in the ROC curve for the device detecting malignancy. - A PPV (Positive Predictive Value) of at least 0.4 and an NPV (Negative Predictive Value) of at least 0.8 for the device detecting malignancy.
Reference of the protocol: DAO Derivación PH 2022	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, longitudinal and pivotal study.	Primary objective: To validate that the information provided by the device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions. Secondary objectives: - Reduce and correct the referral of patients with skin pathologies from primary care to dermatology. - Individualize and improve the ongoing training of general practitioners in the area of dermatology. - Offer healthcare adapted to technological innovations. - Measure the satisfaction of general practitioners with the device. - Measure the satisfaction of dermatologists with the device.	Initiation date: June 24, 2022 Inclusion period: June 24, 2022 to January 10, 2024 This study included 127 patients with different skin conditions. Completion date: January 10, 2024 Date of the study report: January 10, 2024.	- An improvement of diagnostic accuracy of at least 10% (Ferri et al. 2020) in general practitioners and dermatologists. - An AUC of 0.8 detecting malignancy. - A reduction of unnecessary referrals of at least 15% to dermatology (Warshaw et al. 2011). - A positive view of at least 70% of the medical device as a useful tool to gather more patient information.
Reference of the protocol: IDEI 2023	Type of study: Pivotal study State of process: Completed Study design: prospective observational study with both longitudinal and retrospective case series and pivotal study.	Primary objective: To validate that the device optimizes clinical flow and patient care processes, reducing the time and cost of care per patient, through greater precision in medical diagnosis and determination of the degree of malignancy or severity. Secondary objectives: - To demonstrate that the device improves the ability of healthcare professionals to detect malignant or suspected malignant pigmented lesions. - Demonstrate that the device improves the ability and accuracy of healthcare professionals in measuring the degree of involvement of patients with female androgenic alopecia. - Automate the initial triage/assessment process in patients consulting for pigmented lesions. - To evaluate the reduction in the use of healthcare resources by the centre by reducing the number of triage consultations and direct referral of the patient to the appropriate consultation (aesthetic or dermatological). - Evaluate the degree of usability of the device by the patient. - Demonstrate that the device increases specialist satisfaction.	Initiation date: January 25, 2024 Inclusion period: January 25th, 2024 to August 23, 2024 This study included 202 patients with different skin conditions. Completion date: August 23, 2024 Date of the study report: October 20, 2024.	- An improvement of diagnostic accuracy of 10%. - Scores equal to or greater than 70 on the System Usability Scale (SUS). - An AUC equal to or greater than 0.8 detecting malignancy. - A sensitivity equal to or greater than 80% and a specificity equal to or greater than 70% in detecting malignancy. - A correlation (measured with unweighted Kappa) coefficient equal to or greater than 0.5 between the investigator's assessment of the severity of androgenic alopecia and the device's assessment.
Reference of the protocol: MC EVCDAO 2019	Type of study: Pivotal study State of process: Completed Study design: prospective, observational, cross-sectional and pivotal study.	Primary objective: To validate that the device for the identification of cutaneous melanoma in images of lesions taken with a dermatoscopic camera achieves the following values: - AUC greater than 0.8 - Sensitivity of 80% or higher - Specificity of 70% or higher Secondary objectives: - Validate the usefulness and feasibility of the device developed by the manufacturer in adverse environments with severe technical limitations, such as a lack of instrumentation or a lack of internet connection.	Initiation date: February 10, 2020 Inclusion period: February 10, 2020 to November 13, 2023 This study included 105 patients with different skin conditions suspicious of malignancy. Completion date: November 13, 2023 Date of the study report: May 31, 2024.	- An AUC greater than 0.8 - A Sensitivity of 80% or higher - A Specificity of 70% or higher
Reference of the protocol: PH 2024	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, cross-sectional and pivotal study.	Primary objective: To validate that the information provided by the device increases the true accuracy of general practitioners in the diagnosis of multiple dermatological conditions. Secondary objective: - To validate the percentage of cases that should be referred according to the HCP with the information provided by the device. - To validate the percentage of cases that could be handled remotely with the information provided by the device.	Initiation date: June 04, 2024 Inclusion period: June 04, 2024 to September 13rd, 2024 This study included 9 PCPs to assess 30 images of skin conditions. Completion date: September 13rd, 2024 Date of the study report: October 13, 2024.	- An improvement of at least 10% in diagnostic accuracy for skin conditions when used by general practitioners compared to standard clinical practice. - An improvement of at least 14% in diagnostic sensitivity for skin conditions when used by general practitioners compared to standard clinical practice. - An improvement of at least 11% in diagnostic specificty for skin conditions when used by general practitioners compared to standard clinical practice. - An improvement in diagnostic accuracy, sensitivity and specificity of primary care practitioners in the diagnosis of rare dermatological conditions.
Reference of the protocol: SAN 2024	Type of study: Pivotal study State of process: Completed Study design: Prospective, observational, cross-sectional and pivotal study.	Primary objective: To validate that the information provided by the device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions. Secondary objective: - To validate what percentage of cases should be referred according to the HCP with the information provided by the device. - To validate what percentage of cases could be handled remotely with the information provided by the device. - Confirm that the use of the medical device is perceived by specialists as being of great clinical utility.	Initiation date: June 01, 2024 Inclusion period: June 01, 2024 to October 10, 2024 This study included 16 practitioners (6 dermatologists and 10 PCPs) to assess 29 images of skin conditions. Completion date: October 10, 2024 Date of the study report: October 18, 2024	- An improvement of at least 10% in diagnostic accuracy for skin conditions when used by general practitioners, and at least 5% when used by dermatologists, compared to standard clinical practice. - An improvement of at least 14% in diagnostic sensitivity for skin conditions when used by general practitioners, and at least 7% when used by dermatologists, compared to standard clinical practice. - An improvement of at least 11% in diagnostic specificty for skin conditions when used by general practitioners, and at least 10% when used by dermatologists, compared to standard clinical practice.

PMS aspects that need regular updating in the clinical evaluation report

According to the section 7 of the MEDDEV 2.7/1 rev4, the clinical evaluation plan should make it possible to identify PMS aspects that need to be updated in the clinical evaluation report. The following table identifies the PMS aspects that need to be updated in the CER.

This CEP is done for Legit.Health Plus first CE-mark submission (1st commercialisation), therefore such aspects are not yet applicable.

PMS Aspects	Yes	No	N/A
New clinical data available for the device under evaluation	-	-	x
New clinical data available for the equivalent device, if equivalence is claimed	-	-	x
New knowledge about known and potential hazards, risks, performance, benefits and claims, including: _ data on clinical hazards seen in other products (hazard due to substances and technologies); _ changes concerning current knowledge/ the state of the art, such as changes to applicable standards and guidance documents, new information relating to the medical condition managed with the device and its natural course, medical alternatives available to the target population; _ other aspects identified during PMS.	-	-	x

Post-Market Clinical Follow-up (PMCF)

As this CEP is done for Legit.Health Plus first CE-mark submissions (1st commercialisation), and since the manufacturer already benefit from clinical data on the device under evaluation (see section "Confirmatory Phase (Pivotal Investigations)"), at this point of the clinical evaluation, the manufacturer does plan to conduct the PMCF in the first year once the product is on the market.

A PMCF plan (R-TF-007-002 Post-Market Clinical Follow-up (PMCF) Plan) has been prepared according to MDR, Annex XIV Part B, to be applied for the next evaluation, once Legit.Health Plus will be on the market.

Clinical Evidence

The Appendix III of the MDCG 2020-6 guidance document provides a hierarchy of the clinical evidence and considerations to apply, ranked roughly in order from strongest to weakest. The table below presents this hierarchy by listing the types of clinical data used in the context of this clinical evaluation.

Rank	Types of clinical data and evidence	Used?	Type of data used
1	Results of high quality clinical investigations covering all device variants, indications, patient populations, duration of treatment effect, etc.	Yes	Pivotal clinical studies
2	Results of high quality clinical investigations with some gaps	No	NA
3	Outcomes from high quality clinical data collection systems such as registries	No	NA
4	Outcomes from studies with potential methodological flaws but where data can still be quantified and acceptability justified	No	NA
5	Equivalence data (reliable / quantifiable)	No	NA
6	Evaluation of state of the art, including evaluation of clinical data from similar devices	Yes	Published studies in the literature and collected in the state of the art
7	Complaints and vigilance data; curated data	No	NA
8	Proactive PMS data, such as that derived from surveys	No	NA
9	Individual case reports on the subject device	No	NA
10	Compliance to non-clinical elements of common specifications considered relevant to device safety and performance	No	NA
11	Simulated use / animal / cadaveric testing involving healthcare professionals or other end users	No	NA
12	Pre-clinical and bench testing / compliance to standards	Yes	Verification and validation tests (usability, cybersecurity, algorithms' performance, software development,...)

Clinical Concerns

Until now, no clinical concerns have been raised.

If any specific clinical concerns have newly emerged, they will be reported as such in the updated revision of this Clinical Evaluation Plan.

Annexes

Annex I: CV AND DECLARATIONS OF INTEREST.

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

Purpose​

Scope of the clinical plan as part of the clinical evaluation​

References​

Acronyms and definitions​

Acronyms​

Definitions​

Responsibilities - Competence of the Clinical Evaluation Team​

Identification of relevant product requirements​

Description​

Device identification​

Manufacturer identification​

Intended use

Quantification of intensity, count and extent of visible clinical signs

Image-based recognition of visible ICD categories

Device description

Intended medical indication

Intended patient population

Intended user

User qualifications and competencies

Healthcare professionals

IT professionals

Use environment

Operating principle

Body structures

Explainability

Contraindications and precautions required by the manufacturer​

Contraindications​

Precautions​

Warnings​

Undesirable effects​

Intended clinical benefits​

Device classification​

Product category​

Device variants and packaging​

Previous version of the device​

Components​

Mode of action​

Device lifecycle​

Expected lifetime​

Degree of Novelty​

Clinical Performance Claims​

Risk management​

Generic hazards applicable to the device​

Risk mitigation measures​

Safety endpoints​

Acceptability of the benefit-risk ratio​

State of the art​

Scope​

Literature search​

Literature search protocol​

Source of data and search description​

Vigilance databases​

Registres​

Selection Methodology and Criteria​

Literature appraisal data​

Appraisal plan​

Appraisal and weighting criteria​

Clinical Development Plan​

Purpose​

Current State of the Evidence​

Non-clinical Test results - bench testing​

Software Design Verification​

AI Model Validation and Testing​

Usability Engineering​

Existing clinical data​

Confirmatory phase (Pivotal Investigations)​

PMS aspects that need regular updating in the clinical evaluation report​

Post-Market Clinical Follow-up (PMCF)​

Clinical Evidence​

Clinical Concerns​

Annexes​

Purpose

Scope of the clinical plan as part of the clinical evaluation

References

Acronyms and definitions

Acronyms

Definitions

Responsibilities - Competence of the Clinical Evaluation Team

Identification of relevant product requirements

Description

Device identification

Manufacturer identification

Contraindications and precautions required by the manufacturer

Contraindications

Precautions

Warnings

Undesirable effects

Intended clinical benefits

Device classification

Product category

Device variants and packaging

Previous version of the device

Components

Mode of action

Device lifecycle

Expected lifetime

Degree of Novelty

Clinical Performance Claims

Risk management

Generic hazards applicable to the device

Risk mitigation measures

Safety endpoints

Acceptability of the benefit-risk ratio

State of the art

Scope

Literature search

Literature search protocol

Source of data and search description

Vigilance databases

Registres

Selection Methodology and Criteria

Literature appraisal data

Appraisal plan

Appraisal and weighting criteria

Clinical Development Plan

Purpose

Current State of the Evidence

Non-clinical Test results - bench testing

Software Design Verification

AI Model Validation and Testing

Usability Engineering

Existing clinical data

Confirmatory phase (Pivotal Investigations)

PMS aspects that need regular updating in the clinical evaluation report

Post-Market Clinical Follow-up (PMCF)

Clinical Evidence

Clinical Concerns

Annexes