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

Objective

The primary objective of the Post-Market Clinical Follow-up (PMCF) is to systematically gather and meticulously assess data pertaining to the clinical safety and performance of the device, ensuring:

• Ensure Product Integrity: Rigorously confirm the product's safety and performance to maintain the highest standards of clinical efficacy.
• Refine Clinical Assessments: Continually update and enhance the product's clinical evaluation, ensuring its relevance and accuracy over time.
• Uncover and Monitor Adverse Effects: Proactively identify and thoroughly analyze unknown side-effects, while systematically monitoring known side-effects, contraindications, and associated risks.
• Mitigate Emergent Risks: Stay ahead of potential issues by identifying, analyzing, and actively monitoring emergent risks.
• Prevent Product Misuse: Diligently identify potential scenarios of product misuse and implement strategies to prevent them, safeguarding both the product's integrity and user safety.
• Sustain Purpose Adequacy: Regularly verify that the product continues to fulfill its intended purpose effectively, adapting to any changes in user needs or clinical contexts.

Reference to any applicable common specification, harmonized standard or applicable guidance document

When new standards, requirements or guides were applied we will update the corresponing section within the product Technical File.

Common specifications

• Commission Implementing Regulation (EU) 2021/2226 of 14 December 2021 laying down rules for the application of Regulation (EU) 2017/745 of the European Parliament and of the Council as regards electronic instructions for use of medical devices.
• Commission Implementing Regulation (EU) 2021/2078 of 26 November 2021 laying down rules for the application of Regulation (EU) 2017/745 of the European Parliament and of the Council as regards the European Database on Medical Devices (Eudamed).
• Commission Implementing Decision (EU) 2019/939 of 6 June 2019 designating issuing entities designated to operate a system for the assignment of Unique Device Identifiers (UDIs) in the field of medical devices.

Harmonised standards

• UNE-EN ISO 13485:2016 Medical devices. Quality management systems. Requirements for regulatory purposes.
• UNE-EN 62304:2007/CORR:2009/A1:2016 Medical device software. Software life cycle processes.
• UNE-EN ISO 14971:2019 Medical devices/health products. Application of risk management to Medical Devices.
• UNE-EN ISO 15223-1: 2017 Health products. Symbols to be used on labels, labelling and information to be supplied.
• UNE-EN 1041: 2009/A1:2014 Information provided by the manufacturer of medical devices.
• UNE-EN 62366:2009/A1:2015 Application of usability engineering to medical devices.

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: 2024-11-27

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, María Diez, Giulia Foglia
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	1.0.0.0
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
Class	Class IIb
Classification rule	Rule 11
Novel product (True/False)	FALSE
Novel related clinical procedure (True/False)	FALSE
SRN	ES-MF-000025345

Intended use or purpose

Intended use

The device is a computational software-only medical device intended to support health care providers in the assessment of skin structures, enhancing efficiency and accuracy of care delivery, by providing:

• quantification of intensity, count, extent of visible clinical signs
• interpretative distribution representation of possible International Classification of Diseases (ICD) categories.

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,
• dryness,
• oedema,
• oozing,
• excoriation,
• swelling,
• lichenification,
• exudation,
• depth,
• edges,
• undermining,
• pustulation,
• hair loss,
• type of necrotic tissue,
• amount of necrotic tissue,
• type of exudate,
• peripheral tissue edema,
• peripheral tissue induration,
• granulation tissue,
• epithelialization,
• nodule count,
• papule count,
• pustule count,
• cyst count,
• comedone count,
• abscess count,
• draining tunnel count,
• lesion count

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 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 qualification and competencies

In this section we specificy the specific qualifications and competencies needed for users of the device, to properly use the device, provided that they already belong to their professional category. In other words, when describing the qualifications of HCPs, it is assumed that healthcare professionals (HCPs) already have the qualifications and competencies native to their profession.

Healthcare professionals

No official qualifications are needes, but it is advisable if HCPs have some competencies:

• Knowledge on how to take images with smartphones.

IT professionals

IT professionals are responsible for the integration of the medical device into the healthcare organisation's system.

No specific official qualifications are needed, but it is advisable that IT professionals using the device have the following competencies:

• Basic knowledge of FHIR
• Understanding of the output of the device.

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.

Variants and models

The device does not have any variants.

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.

List of any accessories covered by this plan

The device does not have any accessories.

The software can be used from any device with an internet connection.

Explanation of any novel features

The device is the result of an incremental improvement of an existing technology. It has been developed to improve the current state of the art to process photographs of skin structure and then processes them with artificial intelligence algorithms.

We detail the novel features of the devide within the Description and specifications document.

PMCF activities

Activity 1

• Description: Clinical literature review
• Procedure or method: general
• Source: Clinical evaluation report and PMS
• Aim of the Activity: identifying and analysing emergent risks
• Procedures to be used: Monitoring the bibliograpy information as it is explained at the GP-007 Post-market surveillance procedure following the protocol described at the R-TF-015-001 Clinical evaluation plan for the data identification and selection and appraisal.
• Rationale and known limitations of the Activity: There are a high amount of publications throughout all the databases emerging almost in a daily basis that could help us improving our device, that also can be a limitation due to the difficulty to follow all of them.
• Timeline of the activity: The activity will be performed once a year compiling the documents published within the last year.

Activity 2

• Description: PMCF Studies
• Procedure or method: Specific
• Source: Clinical Development Plan and Clinical Evaluation Report
• Overview: These studies provide additional data to support the safety and effectiveness of the device in specific patient populations and under varying clinical conditions.

Pilot study for the clinical validation of an artifical intelligence algorithm to optimize the appropriateness of dermatology referrals

• Description:
  • Design: Prospective observational and analytical study of a longitudinal clinical case series.
  • Sample Size: This study pretends to include 400 patients.
  • Duration: 4 months. An extension of the study has been requested to continue patient recruitment.
• Objective: To validate that Legit.Health artificial intelligence algorithms are a valid tool for optimizing the appropriateness of dermatology referrals.
• Additional information:
  • Location: Health Center Sodupe-Güeñes, Health Center Balmaseda, Health Center Buruaga and Health Center Zurbarán, Spain.
  • Current status: 79 patients enrolled as of september, 2024.
  • Next steps: Continue patient enrollment.
• Rationale: In some cases, there are discrepancies between the diagnoses of primary care physicians and dermatologists ranging from 57% to 65% depending on the study. Data about how Legit.Health can improve the appropriateness of referrals to dermatology will be useful to assess the performance of Legit.Health in a real-world environment.
• Known limitations of the activity: The quantity and quality of the images collected, there is no control group and if the patient does not attend a visit with dermatologist.
• Search period: Study expeceted to finish Q2 2025

Evaluating the performance of Legit.Health in automated triage in teledermatology.

• Description:
  • Design: Observational and retrospective study.
  • Sample size: 30000 images will be analyzed in this study.
• Objective: To assess the impact of implementing Legit.Health in reducing the average waiting time for skin cancer patients and to assess the sensitivity and specifity of Legit.Health detecting malignancy.
• Additional information:
  • Location: Vall d'Hebron University Hospital, Spain.
• Rationale: Obtain performance data of Legit.Health by evaluating the clinical usefulness of instant imaging and the malignancy and urgency level in a population of 30000 referral images from primary care to dermatology.
• Known limitations: Quality of the images and subjectivity in diagnosis.
• Search period: 1 year.

Study for the clinical validation of a medical device for the priorization of consultations in patients with suspected skin cancer.

• Description:
  • Design: Prospective study with intervention.
  • Sample size: 140 patients.
• Objective: To evaluate the impact of the medical device Legit.Health on prioritizing dermatology follow-up consultations in patients with suspected melanoma.
• Additional information:
  • Location: Santa Creu i Sant Pau University Hospital, Spain.
• Rationale: Melanoma is a deadly disease and a late diagnosis reduces the chances of patient survivial. This study pretends to gather data of the performance of Legit.Health priorizing the control visits according to the suspicion of malignancy.
• Known limitations: Quantity and quality of the images, there is no control group, subjectivity in diagnosis and loss of follow-up.
• Search period: 1 year.

Study for the validation of a medical device for improving the diagnosis of skin conditions in Primary Care.

• Description:
  • Design: Prospective study with intervention.
  • Sample size: Still to be confirmed.
• Objective: To validate the medical device Legit.Health to improve the diagnosis of skin conditions in Primary Care and confirmed by dermatology.
• Additional information:
  • Location: Health Center Almozara, Health Center San Pablo, Health Center Revolvería and Miguel Servet University Hospital.
• Rationale: In some cases, there are discrepancies between the diagnoses of primary care physicians and dermatologists ranging from 57% to 65% depending on the study. Data about how Legit.Health can improve the agreement in the diagnoses between primary care and dermatology and improve the appropriateness of referrals.
• Known limitations: Quantity and quality of the images.
• Study period: 14 months.

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

• Description:
  • Design: Prospective study with intervention.
  • Sample size: 30 patients.
• Objective: To validate that the ALADIN severity scale developed by AI LABS GROUP S.L. measures the severity of facial acne with a capacity similar to or greater than that of a specialist, using a photograph taken with a smartphone.
• Additional information:
  • Location: Dermatology DermoMedic Clinic, Spain.
• Rationale: Acne is one of the main reasons for dermatology consultations. This study aims to obtain performance data of Legit.Health by measuring the severity of facial acne.
• Known limitations: Quality of the images, the ALADIN scoring system focuses solely on lesion couting without making distinctions between the different types of lesions.
• Study period: 9 months.

Pilot study for the clinical validation of the SALT automatic system for measuring the severity of alopecia areata based on artificial intelligence.

• Description:
  • Design: Observational and prospective-retrospective study.
  • Sample size: 30 patients.
• Objective: To validate that the automatic SALT severity measurement system for alopecia areata achieves an accuracy equal to or greater than that of an expert clinical using the "gold standard" SALT (Severity of Alopecia Tool).
• Additional information:
  • Location: Still to be confirmed.
• Rationale: This study aims to collect data of Legit.Health performance filling up automatically the scoring system Severity of Alopecia Tool in a real-world environment.
• Known limitations: Quantity and quality of the images which can influence the precission.
• Study period: 3 months.

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

• Description:
  • Design: Observational and retrospective study.
  • Sample size: 100 images from different patients.
• Objective: To validate an automatic measurement system of the Eczema Area and Severity Index (EASI) based on artifical intelligence to determine the severity of atopic dermatitis, and that it does so with an accuracy simlar or better than the consensus of experts who use the "gold standard" EASI.
• Additional information:
  • Location: Virgen de las Nieves University Hospital of Granada.
• Rationale: Atopic Dermatitis is one the most frequent conditions in dermatology. This study aims to collect data of Legit.Health performance filling up automatically the scoring system Eczema Area and Severity Index with images from patients of a real-world environment.
• Known limitations: Quality and quantity of imagens which can influence the precission.
• Study period: 3 months.

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).

• Description:
  • Design: Observational and prospective study.
  • Sample size: 100 patients.
• Objective: To validate that the medical device Legit.Health is capable of measuring the severity of frontal fibrosing alopecia by automatically counting hairs and verify that it does so with a capacity equal or greater than the respective "gold standard" completed by the specialist.
• Additional information:
  • Location: Ramón y Cajal University Hospital of Madrid, Spain.
• Rationale: Measuring the severity of frontal fibrosing alopecia is a subjective process that does not allow for the detection of small changes. This study aims to collect performance data of Legit.Health in measuring the severity of Frontal Fibrosing Alopecia.
• Known limitations: Quality and quantity of images which can influence the precision.
• Study period: 2 years.

Pilot study for the clinical validation of a medical device for the authomatic triage in teledermatology.

• Description:
  • Design: Prospective interventional study.
  • Sample size: Still to be confirmed.
• Objective: To validate that the medical device Legit.Health is capable to priorize the referrals from primary care to dermatology according to the severity.
• Additional information:
  • Location: Vall d'Hebron University Hospital, Spain.
• Rationale: This study aims to collect data about how Legit.Health can improve the referrals from primary care to dermatology according to the suspiction of malignancy.
• Known limitations: The quality and quantitu of images, which can affect the precision, there is no control group, the patient does not attend a visit with dermatologist.
• Study period: 14 months.

Finished investigations

Clinical validation study of a Computer-Aided Diagnosis (CAD) system with artificial intelligence algorithms for early non-invasive detection of in vivo cutaneous melanoma.

• Objective: To validate that the artificial intelligence algorithm developed by AI LABS GROUP S.L. for the identification of cutaneous melanoma in images of lesions taken with a dermoscopic camera achieves the following values:
  • AUC greater than 0.8.
  • Sensitivity of 80% or higher.
  • Specificity of 70% or higher.
• Description:
  • Design: Analytical observational case series study.
  • Target sample size: 200 patients. Finally 105 patients were recruited in the study.
  • Duration: 5 years.
• Results: The AUC metric for the malignancy prediction was 0.88. Regarding skin lesion recognition in general terms, the Top-5 accuracy was 83.97%. Specifically in melanoma, the AUC metric was 84.25%.
• Conclusion: The device demonstrates great malignancy prediction and compelling image recognition capacity for melanoma and other pigmented skin lesions such as carcinoma, keratoses or nevi. Regarding the detection of melanoma, the data collected in this study limits the power of the analysis due to class imbalance, difficult diagnoses, and inconsistent image quality, but the results obtained are compelling even under such challenging conditions.

Optimization of clinical flow in patients with dermatological conditions using Artificial Intelligence.

• Objective: To validate that the device optimizes the clinical flow and patient care process, decreasing the time and cost of care per patient, through greater accuracy in medical diagnosis and determination of the degree of malignancy or severity.
• Description:
  • Design: A prospective observational study with both longitudinal and retrospective case series.
  • Sample size: 66 patients prospectively and 138 retrospectively.
  • Duration: 6 months.
• Results: The medical device demonstrated an AUC of 0.76 in detecting lesion malignancy from retrospective images. It achieved a top-5 accuracy of 0.47 when doing the diagnosis assessment, while the dermatologists achieved a 0.45 of top-3 accuracy. Regarding malignancy analysis, the medical device achieved an AUC of 0.94, the dermatologists 0.95 and these with the medical device an AUC of 0.97. For androgenetic alopecia, we collected 49 retrospective images in addition to 13 previously obtained. The optimized AI model showed a correlation of 0.77 on this earlier dataset. In the prospective test, 34 images were analyzed without any parameter tuning, ensuring an unbiased evaluation of the algorithm's performance. The overall accuracy of the model was 47%, while the accuracy of the latest model optimized for FAA was 53%, based on the investigator's scores.
• Conclusions: The device's diagnostic capability in distinguishing malignancy is on par with expert dermatologists, not only in teledermatology but also in in-person consultations. This confirms its reliability as a screening tool for malignant ICD-11 categories, helping to prioritize patients based on urgency and direct them to the appropriate specialist or consultation.Additionally, we observed a strong correlation in Ludwig scores, despite a decline in the prospective trial, which may be attributed to inconsistencies in criteria alignment. The second part of the study will start in 2025.

Clinical Validation of a Computer-Aided Diagnosis (CAD) System Utilizing Artificial Intelligence Algorithms for Continuous and Remote Monitoring of Patient Condition Severity in an Objective and Stable Manner.

• Objective: The primary aim of this study is to ascertain the validity of the device, leveraging artificial intelligence and 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 in the Clinical Utility Questionnaire (CUS).
• Description:
  • Design: Prospective observational analytical study of a longitudinal clinical case series.
  • Sample size: 160 patients.
  • Duration: 19 months.
• Results: The Clinical Utility Questionnaire responses indicates positive perceptions among specialists, particularly in terms of ease of use and effectiveness in optimizing consultation time according to each patient's needs. The Data Utility questionnaire affirms unanimous agreement on the device's intended usefulness. System Usability Scale results showed high levels of user satisfaction and ease of navigation. Patient satisfaction scores reflected a positive overall experience with the tool.
• Conclusions: The device receives positive feedback from specialists and patients. It also exhibits significant clinical relevance in dermatology. Overall, the device holds promise as a valuable clinical decision support tool for dermatologists.

Project to enhance Dermatology E-Consultations in Primary Care centres using Artifical Intelligence Tools.

• Objective: To validate that the information provided by device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions.
• Description:
  • Design: Prospective observational analytical study of a longitudinal clinical case series.
  • Sample size: 100 patients.
  • Duration: 5 months.
• Results: Legit.Health significantly improved primary care physicians' diagnostic accuracy from 72.96% to 82.22%. The tool showed notable improvements in diagnosing hidradenitis suppurativa, urticaria, and actinic keratosis. Additionally, 49% of cases did not require a referral, and 60.74% could be managed remotely across all specialties.
• Conclusions: The medical device Legit.Health showed an improvement in the diagnostic accuracy in primary care, specially in conditions such as hidradenitis suppurativa or actinic keratosis. The implementation of these technologies can help improve remote patient management and reduce healthcare pressure.

Multicenter pilot study of an artificial intelligence medical device for diagnostic support and severity assessment in primary care and dermatology.

• Objective: To validate that the information provided by device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of generalized pustular psoriasis (GPP).
• Description:
  • Design: Prospective cross-sectional study.
  • Sample Size: 15 healthcare professionals.
  • Duration: 4 months.
• Results: Legit.Health significantly improved the diagnosis of generalized pustular psoriasis (GPP), doubling the diagnosis rate, with a 120% increase in primary care. Overall diagnostic accuracy rose from 47.91% to 62.81% (a 31% increase), and primary care doctors saw a 40% improvement in correct diagnoses. For other conditions like hidradenitis suppurativa and palmoplantar pustulosis, primary care doctors improved by 12.43%, with a 146% increase for palmoplantar pustulosis. Dermatologists also improved, though their data was not statistically significant due to fewer participants and their high expertise, particularly in hidradenitis suppurativa.
• Conclusions: Overall, the tool had a substantial impact, especially in primary care and for rare conditions like GPP. The use of Legit.Health lead to an improvement of the diagnostic accuracy in primary care. This may help to improve the appropriateness of the referrals to dermatology and reduce the healthcare pressure.

Pilot study to evaluate the Performance of a Diagnostic Support Medical Device with Artificial Intelligence.

• Objective: To validate that the information provided by device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions.
• Description:
  • Design: Prospective cross-sectional study.
  • Sample size: 16 healthcare professionals.
  • Duration: 4 months.
• Results: The use of the medical device improved diagnostic accuracy from 68.08% to 88.78%, with primary care physicians seeing an increase from 62.90% to 89.92% and dermatologists from 76.47% to 86.93%. Significant improvements were observed in conditions like tinea, granuloma annulare, and seborrheic keratosis. Around 58.1% of cases required no referral, and 55.11% could be managed remotely, with primary care physicians slightly outperforming dermatologists. Experts agreed on remote management for acne, herpes, and tinea, while melanoma and nevus required in-person care. Additionally, 87% of healthcare professionals found the tool efficient, reducing consultation time to under 10 minutes.
• Conclusions: Legit.Health improved diagnostic accuracy not only in primary care physicians, who saw the largest increase, but also in dermatologists. Significant improvements were observed in conditions like tinea, granuloma annulare, and seborrheic keratosis. Most cases required not referral and could be managed remotely. Additionally, most of healthcare professionals found the tool efficient, reducing consultation time. This could lead to optimize resources and time for the medical consultation.

Activity 3

• Description: Image recognition processor success metrics
• Procedure or method: Specific
• Source: Clinical Evaluation Report and Post-Market Surveillance
• Objective:
  • To meticulously affirm the medical device's performance.
  • To ensure the ongoing validation of the benefit-risk ratio, reinforcing patient safety and device efficacy.
• Methodology:
  • Analyze practitioner-recorded data to validate the output generated by the device.
  • Maintain a rigorous approach to data scrutiny, ensuring accuracy and reliability in performance assessment.
• Rationale and Known Limitations:
  • Practitioner involvement: Practitioners play a crucial role in validating the device's outputs, confirming the suggested diagnoses or providing corrections when necessary. If the correct diagnosis is not present among the top five suggestions, they reassign it accordingly. Our aspiration is to achieve an accuracy aligning with the promising results garnered during the initial clinical evaluation of the device.
  • Varying degrees of specialisation: Different practitioners may have different knowledge of certain conditions. For this analysis, we compared confirmation from practitioners regardless of their level of expertise.
  • Inherent Variability: Challenges arise due to occasional discrepancies in diagnoses even among dermatologists. In rare instances, even dermatopathologists, equipped with advanced microscopy and staining techniques, might not reach a consensus on the precise dermatosis depicted in an image.
• Timeline:
  • This activity is scheduled annually, encapsulating a comprehensive review of the diagnosis confirmations and corrections made by practitioners over the past year.
  • This systematic approach ensures continuous monitoring and quality assurance of the algorithm's performance.

Activity 4

• Description: Similar devices data
• Procedure or method: general
• Source: Clinical evaluation report and PMS
• Aim of the Activity: ensuring the continued acceptability of the benefit-risk ratio
• Procedures to be used: Study and compare the similar devices already detected (see below at the corresponding section of this document Evaluation of clinical data relating to equivalent or similar devices) and described at the first clinical evaluation performed, and look for new or emergent ones. Results will be compiled following the table established at the R-TF-002-005 PMCF report and also at the clinical evaluation perfomance according to the R-TF-015-001 Clinical evaluation plan 2023_001.
• Rationale and known limitations of the Activity: Similar devices in the market can guide us through new interesting features to improve our device, or other emergent risks, unknown side effects or possible misuse.
• Timeline of the activity: The activity will be performed once a year compiling the information regarding similar devices published within the last year.

Reference to relevant parts of the technical documentation

Clinical Evaluation Report

Relevant record name

R-TF-015-003 Clinical evaluation report

Outcome

• Relevant information to be further analysed and monitored: Clinical background, current knowledge, state of the art, clinical data generated and held by us and analysis of all the clinical data.
• No relevant information from the clinical evaluation report to be considered in this plan

Risk Management File

Relevant record name

R-TF-013-001 Risk management plan R-TF-013-002 Risk management record R-TF-013-003 Risk management report

Outcome

• Relevant information to be further analysed and monitored: Residual risks with benefit-risk evaluation acceptable will be monitored to ensure they continue being acceptable.
• No relevant information from the risk management file to be considered in this plan

Evaluation of clinical data relating to equivalent or similar devices

The details about similar devices to Legit.Health Plus discussed in the clinical evaluation report (for which the information about CE marking is available) are provided in the following table.

Product name of similar device	Intended purpose	Intended users	Intended patient population	Medical condition	Indication	Reference to clinical data evaluation in the CER
SkinVision	The SkinVision Service is a software-only, over-the-counter (OTC), mobile medical application, which is intended for use on consumer mobile devices by laypeople. The SkinVision Service consists of an assessment module that generates an immediate indication of risk for the most common types of skin cancer based on a picture of the skin lesion of concern, taken with the app on the mobile device. Based on the assessment, the SkinVision Service provides a recommendation whether to continue monitoring or recommends to visit a health professional for a further review of the skin lesion. The application also facilitates keeping track of skin lesions and provides information on the photographed lesions that may be used when seeking professional healthcare advice. The SkinVision Service augments already existing self-assessment techniques of skin lesions and is not an alternative to healthcare professionals. The SkinVision Service is not intended for use on persons under the age of 18 years old. The SkinVision Service does not diagnose skin cancer, does not provide any other diagnosis.	It is intended for use on consumer mobile devices by laypeople.	Adult patients with suspicion of sufferring from skin cancer or atypical moles	Skin cancer	The SkinVision Service augments already existing self-assessment techniques of skin lesions, and is not an alternative to healthcare professionals.	R-TF-015-003 Clinical evaluation report, sections: Identification of similar devices, Data from Vigilance
MoleScope	MoleScope is a battery-powered dermatoscope intended to be attached to smartphones and tablets to take images of skin, scalp, hair, nail, and other body surfaces with high magnification and cross-polarized light. It is a reusable, non-invasive device for imaging and documentation only.	The device is intended to be used by professional healthcare workers or lay persons.	Patients with suspicion of sufferring from skin cancer or atypical moles	Skin cancer	Intended to be attached to smartphones and tablets to take images of skin, scalp, hair, nail, and other body surfaces.	R-TF-015-003 Clinical evaluation report, sections: Identification of similar devices, Data from Vigilance

Estimated date of the PMCF evaluation report

The T-007-005 PMCF evaluation report will be completed by December 2024 and reviewed yearly.

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