R-TF-007-002 Post-Market Clinical Follow-up (PMCF) Plan

Manufacturer contact details

	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

Medical device characterization

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

Intended use or purpose

Intended use

The device is a computational software-only medical device leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures, enhancing efficiency and accuracy of care delivery, by providing:

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

Quantification of intensity, count and extent of visible clinical signs

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

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

Image-based recognition of visible ICD categories

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

Device description

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

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

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

Intended medical indication

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

Intended patient population

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

Intended user

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

User qualifications and competencies

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

Healthcare professionals

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

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

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

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

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

IT professionals

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

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

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

IT professionals may include, but are not limited to:

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

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

Use environment

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

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

Operating principle

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

Body structures

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

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

Explainability

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

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

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

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

Guidance on PMCF

• MDCG 2020-5 Clinical evaluation - Equivance (04/2020)
• MDCG 2020-7 PMCF plan template (04/2020)
• MDCG 2020-8 PMCF evaluation report (04/2020)

PMCF plan details

• PMCF plan number: 001
• PMCF plan date: 2025-11-10

Rationale and Specific Objectives of the PMCF Plan

The PMCF Plan is an integral part of the Post-Market Surveillance (PMS) system and serves as a continuous update to the Clinical Evaluation Report (CER), as required by Article 83 and Annex XIV, Part B of the MDR.

General Objectives

The overall aim of the PMCF is to confirm the safety and performance, including the clinical benefit of the device, throughout its expected lifetime. Also Identify and analyze emergent risks on the basis of factual evidence. More specifically, the general objectives of the PMCF are to ensure the continued acceptability of the device's Benefit-Risk Ratio and Identify possible systematic misuse or off-label use of the device.

Specific Objectives

To ensure the sustained acceptability of the benefit-risk profile, the latest Clinical Evaluation Report (CER, Version 1.0) requires the PMCF to gather targeted evidence aimed at:

1. Reinforcing existing clinical claims and long-term performance data; and
2. Documenting novel clinical benefits and effectiveness in specific areas like clinical decision support and referral management.

Gap 1: Triage and Malignancy Prioritization. More evidence on the impact on reducing the average waiting time for skin cancer patients.

Gap 2: Automated Severity Assessment and Monitoring for Specific Conditions. Additional clinical data to validate the device's performance in accurately assessing severity and monitoring progression of conditions such as Atopic Dermatitis, Acne, and Frontal Fibrosing Alopecia.

Gap 3: Monitoring of Sustained Core Algorithmic Performance. Ongoing evaluation to ensure the device's core diagnostic algorithms maintain their accuracy and reliability in real-world clinical settings over time.

PMCF activities

The PMCF activities are divided into general methods (proactive data collection) and specific methods (targeted studies).

General PMCF Methods (Drawing Clinical Data from PMS)

These methods represent the routine, continuous collection and evaluation of clinical information derived from the Post-Market Surveillance (PMS) system (as detailed in the PMS Plan). The clinical data gathered through these ongoing activities are formally assessed within the PMCF framework to ensure the continued acceptability of the benefit-risk ratio.

• Gathering Clinical Experience/User Feedback: Continuous collection and evaluation of field reports, service reports, and user surveys to identify performance issues or potential safety concerns in real-world use.
• Systematic Screening of Scientific Literature: Regular and documented searches for new publications related to the device, similar devices, or the underlying technology to identify previously unknown risks or long-term trends.
• Analysis of Clinical Data from PMS: Comprehensive review of all reported incidents, complaints, and vigilance reports (Serious Incidents, FSCA) to detect trends that may impact the clinical benefit or risk profile.
• Evaluation of Public Information: Monitoring registries, regulatory databases (e.g., Eudamed), and public safety communications to gather external real-world evidence.

note

These general activities are defined and explained in detail in the document R-TF-007-001.

Specific PMCF Methods.

The following targeted activities will be undertaken to address the specific objectives listed in Section 15.2 Specific Objectives (Bridging CER Gaps).

A. Consolidated CER Gap 1: Triage and Malignancy Prioritization

Confirmation of Real-World Triage and Prioritization Effectiveness. The PMCF program must generate targeted, post-market clinical data to confirm the clinical effectiveness and operational impact of the device in reducing patient waiting times for high-risk conditions and to validate its accuracy in prioritizing patient follow-up and referrals.

Activity A.1: Legit.Health's performance in automated triage in teledermatology

Code: Legit.Health_triaje_VH_2025

Type: Observational and Retrospective study (30,000 images)

Intended Start Date: February 2026 (Approximate)

Primary Endpoints:

1. Reduction of the average waiting time for skin cancer patients
2. Sensitivity and specificity of Legit.Health in detecting malignancy

Activity A.2: Study for the clinical validation of a medical device for prioritising follow-up visits in patients at risk of melanoma

Code: CVCSD_VC_2402

Type: Prospective and Single-centre study with intervention.

Intended Start Date: January 2026

Primary Endpoint: Prioritisation of follow-up consultations in suspected melanoma lesions by the device.

Activity A.3: Pilot study for the clinical validation of a medical device for the automatic triage in teledermatology

Code: Legit.Health_clinical_VH_2025

Type: Prospective, Multicentre and Interventional study.

Intended Start Date: March 2026.

Primary Endpoint: Validate Legit.Health's capability to prioritise referrals from primary care to dermatology based on severity or the suspicion of malignancy.

B. Consolidated CER Gap 2: Automated Severity Assessment and Monitoring for Specific Conditions

Confirmatory Validation of Severity Assessment and Monitoring Performance. The PMCF program must generate specific clinical evidence to confirm the performance claims regarding the device's capability to accurately measure and quantify the severity of specific dermatological conditions (e.g., Atopic Dermatitis, Acne, and Frontal Fibrosing Alopecia) against clinical Gold Standards.

Activity B.1: Pilot study for the clinical validation of a medical device for the quantification of severity and monitoring of the evolution of patients with FFA (Frontal Fibrosing Alopecia)

Code: LEGIT_AFF_EVCDAO_2021

Type: Observational and Prospective study.

Intended Start Date: April or May 2026.

Primary Endpoint: In this study, the primary endpoint is the agreement between the severity of Frontal Fibrosing Alopecia through counting hairs by the device and the specialist's (gold standard).

Activity B.2: Pilot study for the clinical validation of a medical device for the automatic assessment of severity and remote monitoring of patients with acne

Code: Legit.Health_acne

Type: Observational, Single-centre and Interventional study.

Intended Start Date: March 2026.

Primary Endpoint: The primary endpoint of this study is the agreement (mesured as Interobserver Correlation Coefficient (ICC)) between the acne severity score calculated by the device algorithm (ALADIN) and the expert consensus (gold standard) using smartphone photos.

Activity B.3: Pilot study for the clinical validation of an automatic EASI scoring system with artificial intelligence algorithms to assess the severity of atopic dermatitis

Code: Legit_aEASI_HVN

Type: Retrospective and Single-centre study.

Intended Start Date: February 2026.

Primary Endpoint: The primary endpoint of this study is the agreement (mesured as Interobserver Correlation Coefficient (ICC)) between the Eczema Area and Severity Index (EASI) score calculated by the device algorithm and the expert consensus (gold standard).

_Activity B.4: Prospective Validation of an AI Algorithm for the Automated Calculation of the Vitiligo Area Scoring Index (AVASI) in the Clinical Assessment of Vitiligo.

Code: Legit.Health_AVASI

Type: Prospective, cross-sectional, multicenter and observational study.

Intended Start Date: March 2026.

Primary Endpoint: The primary endpoint is the Intraclass Correlation Coefficient (ICC) for agreement between the Vitiligo Area Scoring Index (VASI) score calculated by the Legit.Health Plus algorithm and the consensus VASI score assessed by an expert dermatologist panel (gold standard).

C. Consolidated CER Gap 3: Core Diagnostic Performance and Stability Monitoring

Monitoring of Sustained Core Algorithmic Performance. The PMCF program must conduct formal, periodic monitoring to demonstrate the sustained stability and effectiveness of the device's core diagnostic algorithms (e.g., accuracy, AUC, Top-N metrics) in the post-market phase, ensuring continued acceptable performance over time.

Activity C.1: Image-based diagnosis non-interventional performance analysis

Code: PMCF-ICD-DXP-2026

Type: Observational, non-interventional study using anonymised data; no direct patient interaction (Reader study).

Intended Start Date: November 2026.

Primary Endpoint: AUC higher than 0.8 for the clinical indicators; Top-5 accuracy higher than 70%; Top-3 accuracy higher than 55%; Top-1 accuracy higher than 40%. (Goal: Monitor/confirm sustained good performance in the market).

Activity C.2: Validation of Legit.Health AI API for Diagnostic Support of Skin Lesions (FDA approval pivotal study real-world performance analysis)

Code: Legit.Health_FDA_Pivotal_RWP_2026

Type: Multireader multicase observational, multicentre, non-interventional study using anonymised data; no direct patient interaction (Reader study) with dermatologist expert panel as Reference.

Intended Start Date: June 2026.

Primary Endpoint: The primary endpoint of this study is the difference in Top-1 and Top-3 diagnostic accuracy between GPs/NPs using Legit.Health Plus (assisted group) and GPs/NPs performing an unassisted visual assessment (unassisted group) and the agreement with an expert dermatologist panel, who will act as a goldstandard.

Reference to relevant parts of the technical documentation

The objectives and methodology of this PMCF Plan are directly derived from the data, conclusions, and residual risks identified in the following technical documents:

• R-TF-015-003 Clinical Evaluation Report.
• R-TF-015-006 Series - Pre-market Clinical Investigation Reports.
• R-TF-013-002 Risk management record.
• Description and specifications.
• R-TF-001-008 Label.
• R-TF-025-004 Summative evaluation protocol.
• R-TF-007-001 Post-Market Surveillance (PMS) Plan.
• Instructions for use.

Estimated date of the PMCF evaluation report

The results and conclusions of the PMCF activities will be documented in the PMCF Evaluation Report. This report will form an integral part of the Periodic Safety Update Report (PSUR), in accordance with Article 86 and Annex XIV, Part B, Section 5 of Regulation (EU) 2017/745. The PSUR will be generated after the first year of market placement of the device and updated on an annual basis, as required for Class IIb devices

Estimated completion date: January 2027.

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