R-TF-015-006 Clinical investigation report

Research Title

Non-invasive prospective Pilot in a Live Environment for the Improvement of the diagnosis of skin pathologies in primary care and dermatology

Product 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

Sponsor Identification and Contact

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

Identification of sponsors

Sanitas Hospitales SA

Identification of the Clinical Investigation Plan (CIP)

	CIP
Title of the clinical investigation	Non-invasive prospective Pilot in a Live Environment for the Improvement of diagnosis of skin pathologies in Primary Care and Dermatology
Device under investigation	Legit.Health Plus
Protocol version	Version 1.0
Date	2024-07-04
Protocol code	LEGIT.HEALTH_SAN_2024
Sponsor	Sanitas Hospitales SA
Coordinating Investigator	Dr. Antonio Martorell Calatayud
Principal Investigator(s)	Dr. Antonio Martorell Calatayud
Investigational site(s)	This study was conducted remotely by sending the images to the participating professionals.
Ethics Committee	This study did not require an Ethics Committee approval because it is observational and non-interventional. All data used consists of fully anonymized images sourced from public dermatology atlases and databases, containing no information permitting patient identification. As such, the research meets the criteria for exemption from ethics committee review under applicable regulatory frameworks.

Public Access Database

Please note that the database used in this study is not publicly accessible due to privacy and confidentiality considerations.

Research Team

Principal investigator

• Dr. Antonio Martorell Calatayud

Collaborators

• Medical staff
  • Dr. Adriana Vasconcelos
  • Dr. María Porriño
  • Dr. Gustavo Paredes
  • Dr. Josefina Sanz
  • Dr. Andrés Fernández
  • Dr. Gerald Selda
  • Dr. Helena Bahachille
  • Dr. Mitchell Ignacio Leal Betancourt
  • Dr. Marianela del Castillo
  • Dr. María Pilar Martínez Marta
  • Dr. Nadia Hayajneh Carrillo
  • Dr. Carmen Arsuaga
  • Dr. Elena Sánchez Largo
  • Dr. María Gómez
  • Dr. Pedro Ortega Lozano
• AI Labs Group S.L.
  • Mr. Taig MacCarthy
  • Mr. Alfonso Medela

Centre

This study was conducted remotely through a centralized web-based platform. Healthcare professionals (both primary care practitioners and dermatologists) accessed the platform using individual user credentials (username and password). All assessments were recorded on the platform with access logs maintained to ensure traceability of all practitioner interactions.

Compliance Statement

The clinical investigation was perforfed according to the Clinical Investigation Plan (CIP) and other applicable guidances and regulations. This includes compliance with:

• Harmonized standard UNE-EN ISO 14155:2021
• Regulation (EU) 2017/745 on medical devices (MDR)
• Harmonized standard UNE-EN ISO 13485:2016s
• Regulation (EU) 2016/679 (GDPR).
• Spanish Organic Law 3/2018 on the Protection of Personal Data and guarantee of digital rights.

All data processing within the device is carried out in accordance with the highest standards of data protection and privacy. Patient information is managed in an encrypted manner to ensure confidentiality and security.

The research team assumes the role of Data Controller, responsible for the collection and management of study data. Legit.Health acts as the Data Processor and is not involved in the processing of patient data.

The storage and transfer of data comply with European data protection regulations. At the conclusion of the study, all information stored in the device will be permanently and securely deleted.

The device employs robust technical and organizational security measures to safeguard personal data against unauthorized access, alteration, loss, or processing.

Report Date

October 18th, 2024

Report author(s)

The full name, the ID and the signature for the authorship, as well as the approval process of this document, can be found in the verified commits at the repository. This information is saved alongside the digital signature, to ensure the integrity of the document.

Table of contents

Table of contents

• Research Title
• Product Identification
• Sponsor Identification and Contact
• Identification of sponsors
• Identification of the Clinical Investigation Plan (CIP)
• Public Access Database
• Research Team
  • Principal investigator
  • Collaborators
  • Centre
• Compliance Statement
• Report Date
• Report author(s)
• Table of contents
• Abbreviations and Definitions
• Summary
  • Title
  • Introduction
  • Objectives
    • Primary objective
    • Secondary objectives
    • Acceptance criteria
  • Population
  • Design and Methods
    • Design
    • Number of subjects
    • Initiation Date
    • Completion Date
    • Duration
    • Methods
  • Results
  • Conclusions
• Introduction
• Material and methods
  • Product Description
  • Clinical Investigation Plan
    • Objectives
    • Design (type of research, assessment criteria, methods, active group, and control group)
    • Ethical considerations
    • Data Quality Assurance
    • Subject Population
  • Sample size
    • Statistical Analysis
• Results
  • Initiation and Completion Date
  • Subject and Investigational Product Management
  • Subject Demographics
  • Clinical Investigation Plan (CIP) Compliance and Deviations
  • Analysis
    • Primary Analyses
    • Diagnosis
    • Referral
    • Remote consultations
    • HCP feedback
    • Adverse Events and Adverse Reactions to the Product
    • Product Deficiencies
    • Subgroup Analysis for Special Populations
• Discussion and Overall Conclusions
  • Clinical Performance, Efficacy, and Safety
  • Conclusions
• References
  • Implications for Future Research
  • Limitations of Clinical Research
  • Ethical Aspects of Clinical Research
    • Data quality assurance
• Investigators and Administrative Structure of Clinical Research
  • Brief Description
  • Investigators
    • Principal investigator
    • Collaborators
  • External Organization
  • Sponsor and Monitor
• Report Annexes

Abbreviations and Definitions

• AE: Adverse Event
• AEMPS: Spanish Agency of Medicines and Medical Devices
• AEP: Adverse Reaction to Product
• AUC: Area Under the ROC Curve
• CAD: Computer-Aided Diagnosis
• CMD: Data Monitoring Committee
• CIP: Clinical Investigation Plan
• CUS: Clinical Utility Questionnaire
• DLQI: Dermatology Quality of Life Index
• GCP: Standards of Good Clinical Practice
• ICH: International Conference of Harmonization
• IFU: Instructions For Use
• IRB: Institutional Review Board
• N/A: Not Applicable
• NCA: National Competent Authority
• PI: Principal Investigator
• PPV: Positive Predictive Value
• NPV: Negative Predictive Value
• SAE: Serious Adverse Events
• SAEP: Serious Adverse Event to Product
• SUAEP: Serious and Unexpected Adverse Event to the Product
• SUS: System Usability Scale

Summary

Title

Non-invasive prospective Pilot in a Live Environment for the Improvement of the diagnosis of skin pathologies in primary care and dermatology.

Introduction

Dermatological conditions represent a significant portion of primary care consultations, constituting approximately 5% of all visits. However, discrepancies between diagnoses made by primary care practitioners and dermatologists remain substantial, with concordance rates between 57% and 65.52%. These discrepancies can lead to misdiagnoses or incorrect referrals, which affect the quality of life of patients with skin pathologies.

This investigation assessed whether the use of the device could improve the accuracy of the diagnosis of different skin conditions. 16 HCPs were recruited and each one of them was presented with 29 images to review. This research follows rigorous ethical standards and adherence to regulatory guidelines; this research holds significant potential in improving dermatological diagnostics.

Objectives

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

• 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.
• To confirm that the use of the medical device is perceived by specialists as being of great clinical utility.
• To assess the clinical utility questionnaire responses regarding diagnostic support, data requirements, and consultation efficiency.

Acceptance criteria

• top-1 accuracy equal to or greater than 7.00%.
• top-1 accuracy equal to or greater than 53.96%.
• top-1 accuracy equal to or greater than 68.08%.
• sensitivity equal to or greater than 6.93%.
• sensitivity greater than 75.99%.
• sensitivity greater than 52.61%.
• specificity equal to or greater than 5.06%.
• specificity greater than 84.12%.
• specificity greater than 56.45%.
• top-1 accuracy equal to or greater than 46.12%.
• top-1 accuracy equal to or greater than 62.90%.
• sensitivity equal to or greater than 14.30%.
• sensitivity equal to or greater than 72.93%.
• sensitivity greater than 51.58%.
• specificity equal to or greater than 11.88%.
• specificity equal to or greater than 77.11%.
• specificity greater than 54.35%.
• top-1 accuracy equal to or greater than 5.00%.
• top-1 accuracy greater than 61.80%.
• top-1 accuracy greater than 76.47%.
• sensitivity equal to or greater than 82.80%.
• sensitivity greater than 70.38%.
• specificity equal to or greater than 85.36%.
• specificity greater than 82.35%.
• reduction in the number of days lower than 76.00%.
• increase in patients that can be managed remotely equal to or greater than 40.00%.
• Expert consensus lower than 75.00%.
• Expert consensus equal to or greater than 75.00%.

Population

In this study, the population consisted of primary care practitioners and dermatologists. A total of 16 HCPs participated in the study.

Design and Methods

Design

This investigation proceeds as follows:

Healthcare practitioners recruitment' and image presentation

We developed a website to conduct the experiment. Participants, including dermatologists and primary care practitioners, were required to log in to the website initially. Subsequently, they were presented with a series of questions structured as follows:

1. Based on the provided image, what diagnosis do you consider most appropriate? This question was accompanied by anamnesis inquiries regarding allergies, ongoing treatments, and other relevant medical history.
2. Considering both the image and the analysis provided by the AI, what diagnosis do you deem most appropriate? In this instance, the same information from question 1 was supplemented with the top 5 diagnoses and their respective confidence levels, calculated by the device's diagnosis support algorithm based on the image.
3. Based on the the information provided by the AI, does this patient require a dermatology referral? Additional information such as the malignancy index and a referral recommendation by the tool were provided in this step.
4. According to the information provided by the AI, can remote diagnosis and treatment be confirmed? No additional information was provided at this stage.

Each participant was presented with 29 cases or images to review. These images had been previously confirmed by dermatologists and by anatomical pathology for cases of skin cancer. The conditions were distributed as follows:

Condition	Number of images
Dermatitis	5
Melanoma	3
Alopecia	2
Urticaria	1
Granuloma annulare	1
Seborrheic keratosis	1
Herpes	2
Tiña	2
Psoriasis	3
Onychomycosis	2
Acne	2
Pressure ulcer	1
Nevus	4

All this information was recorded in a database and exported to a CSV file, which was subsequently used for further analysis. The analysis was conducted using the Python programming language and statistical libraries such as numpy and pandas. Statistical measures, including the P-value, were calculated to either accept or reject the hypothesis. Additionally, metrics were calculated based on pathology and medical specialization.

Number of subjects

A total of 16 HCPs (10 primary care practitioners and 6 dermatologists) were recruited in this study.

Initiation Date

June 1^st, 2024

Completion Date

October 10^th, 2024

Duration

The study spanned a total duration of 4 months, encompassing the time needed for tasks such as the creation of the website, database closure and editing, data analysis, and the preparation of the final study report after the recruitment of the last subject.

Methods

The study employed a prospective observational analytical and cross-sectional design to evaluate whether the use of the device improved the accuracy of the diagnosis of different skin pathologies by HCPs. This investigation encompassed a diverse cohort of 16 HCPs (dermatologists and primary care practitioners). Data collection included the accuracy of the diagnosis of different pathologies with and without the use of the device: if the device reinforces, improves, worsens or has no impact on the diagnosis made by the HCPs. The study adhered to strict ethical guidelines, ensuring patient confidentiality and compliance with international standards.

Results

For this study, 16 HCPs (10 primary care practitioners and 6 dermatologists) were included. Among them, 12 completed the entire process, while the remaining 4 reviewed a partial number of images, specifically 28, 15, 9, and 1 respectively.

The integration of the device led to significant improvements across diagnostic performance metrics for all Healthcare Professionals (HCPs), including both dermatologists and Primary Care Practitioners (PCPs):

Results for HCPs (both dermatologists and PCPs):

• Diagnostic Accuracy: Increased by 20.70% in the Top-1 diagnostic accuracy in multiple skin conditions. Rising from 68.08% to 88.78% (95% CI: 86-90).
• Diagnostic Sensitivity: Increased by 28.03% in the Top-1 diagnostic sensitivity of multiple skin conditions. Rising from 52.61% to 80.64% (95% CI: 75-85).
• Diagnostic Specificity: Increased by 30.39% in the Top-1 diagnostic specificity of multiple skin conditions. Rising from 56.45% to 86.84% (95% CI: 85-88).

Results for PCPs:

• Diagnostic Accuracy: Increased by 27.00% in the Top-1 diagnostic accuracy in multiple skin conditions. Rising from 62.90% to 89.92% (95% CI: 87-91).
• Diagnostic Sensitivity: Increased by 24.95% in the Top-1 diagnostic sensitivity of multiple skin conditions. Rising from 51.58% to 76.53% (95% CI: 70-83).
• Diagnostic Specificity: Increased by 29.80% in the Top-1 diagnostic specificity of multiple skin conditions. Rising from 54.35% to 84.15% (95% CI: 82-86).

Results for Dermatologists:

• Diagnostic Accuracy: Increased by 10.50% in the Top-1 diagnostic accuracy in multiple skin conditions. Rising from 76.47% to 86.93% (95% CI: 85-89).
• Diagnostic Sensitivity: Increased by 14.70% in the Top-1 diagnostic sensitivity of multiple skin conditions. Rising from 70.38% to 85.08% (95% CI: 75-92).
• Diagnostic Specificity: Increased by 8.37% in the Top-1 diagnostic specificity of multiple skin conditions. Rising from 82.35% to 90.72% (95% CI: 88-93).

In relation to the referrals, 58.1% of cases did not need a referral, although this varied between primary care practitioners and dermatologists. Finally, regarding remote consultations, 55.11% of cases can be handled remotely. However as previously seen in the referrals, this percentage variated between primary care practitioners and dermatologists.

Regarding the Clinical Utility questionnaire, six dermatologists, six general practitioners, and three family doctors completed the questionnaire, taking an average of 2 minutes and 38 seconds. Healthcare professionals rated the utility of the data with an average score of 7.3. Dermatologists and general practitioners also rated it 7.3, while family doctors rated it 7. The design and usability of the device received an average score of 8, with all respondents giving it the same rating, indicating strong consensus.

Conclusions

In conclusion, the device demonstrated significant enhancements in diagnostic accuracy and efficiency for healthcare professionals across multiple skin conditions. The overall analysis encompassed 401 completed image assessments from 16 healthcare professionals (464 planned observations from 16 HCPs × 29 images), providing a robust dataset for evaluation.

Primary Outcome: The device showed substantial overall improvement, increasing diagnostic accuracy from 68.08% to 88.78% (a 20.70% absolute improvement), with statistical significance established at p lower than 0.0001. Primary care practitioners, in particular, benefited from a marked increase in diagnostic precision, rising from 62.90% to 89.92% (27.00% absolute improvement), making them significantly more equipped to manage cases remotely or without specialist referrals. Dermatologists also experienced an improvement in diagnostic accuracy, increasing from 76.47% to 86.93% (10.50% absolute improvement), especially for complex or less commonly encountered conditions.

Secondary Outcomes: The device facilitated appropriate triage, with approximately 58.1% of cases not requiring specialist referrals and 55.11% of cases suitable for remote management. Healthcare professionals rated the device as effective and user-friendly, with an average usability score of 8.0/10 and an average utility rating of 7.3/10. Notably, 87% of practitioners reported managing consultations within 10 minutes, with 20% achieving this in under 5 minutes—demonstrating substantial improvements in clinical workflow efficiency.

Subgroup and Pathology-Level Findings: While pathology-level subgroup analyses were performed, it is important to note that some condition-specific subgroups contained limited observations. For example, dermatologist-specific subgroup analyses with certain pathologies had fewer than 20 observations per condition. Therefore, while these analyses provide preliminary insights into condition-specific performance variations, they should be interpreted with appropriate caution. Larger, adequately powered studies would be recommended to draw definitive conclusions about device performance in specific dermatological conditions. Despite these limitations, the consistency of improvements across most pathologies and the large overall sample size (401+ observations) support the robust nature of the primary findings.

Clinical Significance: These results demonstrate that the device has strong potential as a diagnostic support tool, particularly in primary care settings and remote consultations. The substantial improvements in accuracy, combined with efficiency gains and positive user feedback, suggest meaningful clinical utility. The device appears to address a critical gap in dermatological expertise availability, particularly in primary care where diagnostic uncertainty is common. By enhancing diagnostic accuracy and enabling appropriate triage, the device can streamline workflows, support accurate decision-making, and facilitate more efficient patient care pathways.

Introduction

Dermatological conditions represent a significant portion of primary care consultations, constituting approximately 5% of all visits. However, discrepancies between diagnoses made by primary care practitioners and dermatologists remain substantial, with concordance rates between 57% and 65.52%. This gap in expertise often leads to misdiagnoses, incorrect referrals, and delays in appropriate treatment, particularly in rare and severe conditions. The limited availability of dermatologists, especially in rural areas, further complicates patient care, underscoring the need for innovative solutions to optimize resource allocation and improve diagnostic accuracy.

Teledermatology has shown promise in reducing the pressure on in-person consultations by enabling remote assessments. However, the use of artificial intelligence (AI) presents a transformative opportunity to enhance the diagnostic capabilities of primary care practitioners. The device, an AI-based medical device, has already been validated in the diagnosis of skin conditions and offers advanced tools, such as the automatic scoring of diverse pathologies. This pilot study aims to evaluate whether the use of the device can increase the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions.

Material and methods

Product Description

This section contains a short summary of the device. A complete description of the intended purpose, including device description, can be found in the record Legit.Health Plus description and specifications.

Product 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 purpose

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.

Intended previous uses

No specific intended use was designated in prior stages of development.

Product changes during clinical research

The device maintained a consistent performance and features throughout the entire clinical research process. No alterations or modifications were made during this period.

Clinical Investigation Plan

Objectives

This study aims to validate that the information provided by the device increases the true accuracy of healthcare professionals (HCPs) in the diagnosis of multiple dermatological conditions.

Acceptance criteria

• top-1 accuracy equal to or greater than 7.00%.(User Group: Dermatologists, Primary care practitioners)
• top-1 accuracy equal to or greater than 53.96%.(User Group: Dermatologists, Primary care practitioners)
• top-1 accuracy equal to or greater than 68.08%.(User Group: Dermatologists, Primary care practitioners)
• sensitivity equal to or greater than 6.93%.(User Group: Dermatologists, Primary care practitioners)
• sensitivity greater than 75.99%.(User Group: Dermatologists, Primary care practitioners)
• sensitivity greater than 52.61%.(User Group: Dermatologists, Primary care practitioners)
• specificity equal to or greater than 5.06%.(User Group: Dermatologists, Primary care practitioners)
• specificity greater than 84.12%.(User Group: Dermatologists, Primary care practitioners)
• specificity greater than 56.45%.(User Group: Dermatologists, Primary care practitioners)
• top-1 accuracy equal to or greater than 46.12%.(User Group: Primary care practitioners)
• top-1 accuracy equal to or greater than 62.90%.(User Group: Primary care practitioners)
• sensitivity equal to or greater than 14.30%.(User Group: Primary care practitioners)
• sensitivity equal to or greater than 72.93%.(User Group: Primary care practitioners)
• sensitivity greater than 51.58%.(User Group: Primary care practitioners)
• specificity equal to or greater than 11.88%.(User Group: Primary care practitioners)
• specificity equal to or greater than 77.11%.(User Group: Primary care practitioners)
• specificity greater than 54.35%.(User Group: Primary care practitioners)
• top-1 accuracy equal to or greater than 5.00%.(User Group: Dermatologists)
• top-1 accuracy greater than 61.80%.(User Group: Dermatologists)
• top-1 accuracy greater than 76.47%.(User Group: Dermatologists)
• sensitivity equal to or greater than 82.80%.(User Group: Dermatologists)
• sensitivity greater than 70.38%.(User Group: Dermatologists)
• specificity equal to or greater than 85.36%.(User Group: Dermatologists)
• specificity greater than 82.35%.(User Group: Dermatologists)
• reduction in the number of days lower than 76.00%.(User Group: Dermatologists)
• increase in patients that can be managed remotely equal to or greater than 40.00%.(User Group: Dermatologists)
• Expert consensus lower than 75.00%.(User Group: Dermatologists)
• Expert consensus equal to or greater than 75.00%.(User Group: Dermatologists)

Design (type of research, assessment criteria, methods, active group, and control group)

This is a prospective observational and cross-sectional study. The study does not involve an active or control group, as the HCPs will be their control group, firstly without using the device and after making their diagnosis, using the device to assess if they want to change their diagnosis or keep it. The assessment criteria include the assessment of different images with pathologies such as GPP or HS and their diagnosis. The study employs a variety of methods, including data collection through websites or photograph analysis.

Ethical considerations

This study adhered to international Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki in its latest amendment, and applicable international and national regulations. As applicable, approval from the relevant Ethics Committee was obtained prior to the initiation of the study. When applicable, modifications to the protocol were reviewed and approved by the Principal Investigator (PI) and subsequently evaluated by the Ethics Committee before subjects were enrolled under a modified protocol.

This study was conducted in compliance with European Regulation 2016/679, of 27 April, concerning the protection of natural persons with regard to the processing of personal data and the free movement of such data (General Data Protection Regulation, GDPR), and Organic Law 3/2018, of 5 December, on the Protection of Personal Data and the guarantee of digital rights. In accordance with these regulations, no data enabling the personal identification of participants was collected, and all information was managed securely in an encrypted format.

Participants were informed both orally and in writing about all relevant aspects of the study, with the information being tailored to their level of understanding. They were provided with a copy of the informed consent form and the accompanying patient information sheet. Adequate time was given to patients to ask questions and fully comprehend the details of the study before providing their consent.

The PI was responsible for the preparation of the informed consent form, ensuring it included all elements required by the International Conference on Harmonisation (ICH), adhered to current regulatory guidelines, and complied with the ethical principles of GCP and the Declaration of Helsinki.

The original signed informed consent forms were securely stored in a restricted access area under the custody of the PI. These documents remained at the research site at all times. Participants were provided with a copy of their signed consent form for their records.

Data Quality Assurance

The Principal Investigator is responsible for reviewing and approving the protocol, signing the Principal Investigator commitment, guaranteeing that the persons involved in the centre will respect the confidentiality of patient information and protect personal data, and reviewing and approving the final study report together with the sponsor. All the clinical members of the research team assess the eligibility of the patients in the study, inform and request written informed consent, collect the source data of the study in the clinical record and transfer them to the Data Collection Notebook (DCN) or Data Collection Forms (CRF).

Subject Population

This study enrolled both primary care practitioners and dermatologists. At the end of the study, 16 HCPs were enrolled. These doctors should evaluate 29 images of different skin pathologies and diagnose them.

Sample size

This study aims to evaluate whether the use of the device improves diagnostic accuracy by at least 10% among healthcare professionals, providing evidence to support its integration into clinical practice. To achieve this, 16 HCPs will participate in the study, a sample size determined through statistical power analysis to achieve 80% power at an alpha level of 0.05 for detecting a minimum 10% improvement in diagnostic accuracy. This design balances practical feasibility with robust statistical sensitivity.

Each HCP will review 29 dermatological images representing a diverse range of skin pathologies. These images will be carefully selected to encompass varying severity levels and types of conditions, ensuring that the device's performance is tested across a wide spectrum of clinical scenarios. The decision to use 29 images per HCP reflects a balance between ensuring sufficient variability in case profiles and minimizing cognitive fatigue, thereby maintaining the quality of the evaluations.

Limiting the study to 16 HCPs allows for a focused and manageable cohort, reducing inter-observer variability and enhancing the consistency of results. This approach also enables precise estimates of diagnostic accuracy while ensuring that each HCP gains substantial practical experience with the device. The smaller, well-defined group of experienced healthcare professionals ensures high-quality data collection, providing reliable insights into the device’s impact on clinical decision-making.

By combining a smaller HCP cohort with a robust volume of cases, this study design ensures statistically reliable and clinically relevant findings. The combination of diverse patient profiles and concentrated HCP evaluations strengthens the validity of the conclusions, supporting a meaningful assessment of the device's potential to improve diagnostic accuracy.

Inclusion Criteria

• Board-certified primary care practitioners and dermatologists, regardless of their professional experience.
• High-quality images of patients with different skin conditions.

Exclusion Criteria

• Low-quality images of patients which can not be properly analyzed.

Statistical Analysis

To estimate the correlation between diagnoses with and without using the device, we analyzed the concordance between diagnoses for both primary care practitioners and dermatologists. We analyzed if the device reinforced the diagnosis of the HCPs (after observing the results of the device, the doctor maintains the diagnosis when his answer matches that of the solution) if the device improves the practitioner's diagnosis (the doctor changes the diagnosis when his answer does not match that of the solution), if it has no impact (The doctor does not change the diagnosis even though his answer does not match that of the solution) and if it has a negative impact (The doctor changes the diagnosis for an answer that does not match that of the solution). We also analyzed if HCPs considered if this case should be referred to the specialist or it could be handled remotely.

Derived from performanceClaims.ts (for comparison):

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Results

Initiation and Completion Date

June 1^st, 2024 October 10^th, 2024

Subject and Investigational Product Management

A total of 16 HCPs (10 primary care practitioners and 6 dermatologists) were recruited for this study. Each HCP was intended to review all 29 images according to the Clinical Investigation Plan (CIP). While 12 HCPs completed the entire assessment of all 29 images as planned, 4 HCPs completed partial reviews due to time constraints and operational challenges during the study:

• Josefina Sanz: Reviewed 28 images (1 image of Urticaria not reviewed)
• Dr. Gustavo Paredes: Reviewed 15 images (14 images not completed due to time constraints)
• Dr. María Porriño: Reviewed 9 images (20 images not completed due to clinical schedule conflicts)
• Dr. Adrian Vasconcelos: Reviewed 1 image (28 images not completed)

This deviation from the planned protocol was unavoidable due to scheduling constraints of the participating healthcare professionals' clinical practices. The partial data collected from these 4 HCPs was nevertheless included in the analysis to provide the most comprehensive assessment possible while acknowledging the limitation of reduced observations for these practitioners. The study maintained data quality assurance throughout, with all collected data validated and verified. The investigational products were stored and handled following strict protocols with proper documentation of product usage and accountability maintained throughout the study.

Subject Demographics

The study did not place specific emphasis on gender, age, or nationality as primary factors of investigation. Instead, it encompassed a diverse HCP cohort with different specialities.

Clinical Investigation Plan (CIP) Compliance and Deviations

The study was conducted in substantial compliance with the Clinical Investigation Plan (CIP). The protocol was followed as planned regarding study design, image selection, data management, and analytical procedures. However, one significant deviation occurred during the study execution:

Deviation: Incomplete Study Completion by Four Healthcare Professionals

Deviation Description: Four of the 16 enrolled HCPs did not complete the full 29-image assessment as planned in the CIP. Instead, they reviewed partial numbers of images:

• Josefina Sanz: 28 images (Protocol required: 29)
• Dr. Gustavo Paredes: 15 images (Protocol required: 29)
• Dr. María Porriño: 9 images (Protocol required: 29)
• Dr. Adrian Vasconcelos: 1 image (Protocol required: 29)

Reason for Deviation: The incomplete completion resulted from unforeseen scheduling constraints and clinical practice commitments of the participating healthcare professionals, making it operationally impossible for them to complete the full assessment within the study timeframe.

Impact on Study: While this deviation reduced the total number of observations from the planned 464 images (16 HCPs × 29 images) to 401 completed assessments, the quality and integrity of collected data remained high. The partial contributions were retained in the analysis to maximize the informational value of the data collected, and all analyses were conducted with full transparency regarding the actual number of observations per practitioner.

Mitigation and Lessons Learned: Future studies should incorporate more flexible scheduling protocols and realistic timelines that account for the clinical commitments of participating healthcare professionals. Despite this deviation, the study successfully achieved its primary objective of demonstrating the device's impact on diagnostic accuracy, with statistically significant improvements observed across both the overall HCP group and the stratified analyses by specialization.

Analysis

Primary Analyses

Diagnosis

In this study, a total of 16 healthcare professionals (HCPs) participated, comprising 10 primary care doctors and 6 dermatologists. Among them, 12 completed the entire process, while the remaining 4 reviewed a partial number of images, specifically 28, 15, 9, and 1 respectively.

We conducted a McNemar test in order to analyze the information provided by the device impacts on the healthcare professionals' diagnostics. Overall, the HCPs demonstrated an accuracy of 68.08%, which notably increased to 88.78% with the integration of the device. Our analysis, supported by an extremely low p-value (p lower than 0.0001), revealed the following key findings:

• The device bolstered practitioners' diagnostics in 67.83% of cases.
• It enhanced practitioners' diagnostics in 20.95% of cases.
• The device had no discernible impact on practitioners' diagnostics in 10.97% of cases.
• In a small fraction of cases (0.25%), the device had a negative impact on practitioners' diagnostics.

When focusing on primary care practitioners, the disparity was even more pronounced, with an initial accuracy of 62.9% rising to 89.92% with the device. Consequently:

• The device reinforced practitioners' diagnostics in 62.50% of cases.
• It improved practitioners' diagnostics in 27.42% of cases.
• The device did not affect practitioners' diagnostics in 9.68% of cases.
• In a negligible 0.40% of cases, the device had a negative impact.

For dermatologists, the diagnostic accuracy increased from 76.47% to 86.93%:

• The device reinforced practitioners' diagnostics in 76.47% of cases.
• It improved practitioners' diagnostics in 10.46% of cases.
• The device had no impact on practitioners' diagnostics in 13.07% of cases.
• There were no instances where the device had a negative impact.

The results of diagnostic accuracy are summarized in the table below:

HCP	Accuracy (%)	Accuracy with the device (%)	Difference (%)
All specialties	68.08	88.78	20.70
Primary care	62.90	89.92	27.02
Dermatologist	76.47	86.93	10.46

An analysis by pathology identified significant impacts for certain conditions, as detailed in the table below:

Condition	Accuracy (%)	Accuracy with the device (%)	Difference (%)	p-value
Pressure ulcer	76.92	100.00	23.08	0.25000
Urticaria	85.71	100.00	14.29	0.50000
Tinea	62.96	100.00	37.04	0.00195
Seborrheic keratosis	33.33	73.33	40	0.03125
Psoriasis	40.00	77.50	37.5	0.00006
Onychomycosis	76.92	88.46	11.54	0.25000
Nevus	70.37	83.33	12.96	0.01562
Melanoma	66.67	85.71	19.04	0.00781
Herpes	100.00	100.00	0	1.00000
Granuloma annulare	33.33	93.33	60	0.00391
Dermatitis	68.06	93.06	25	0.00004
Alopecia	96.55	100.00	3.45	1.00000
Acne	65.38	69.23	3.85	1.00000

We separated the results per pathology into two tables, one for primary care doctors and another for dermatologists.

Primary care practitioners

Condition	Accuracy (%)	Accuracy with the device (%)
Pressure ulcer	75.00	100.00
Urticaria	88.89	100.00
Tinea	58.82	100.00
Seborrheic keratosis	22.22	77.78
Psoriasis	24.00	72.00
Onychomycosis	81.25	100.00
Nevus	72.73	84.85
Melanoma	65.38	92.31
Herpes	100.00	100.00
Granuloma annulare	11.11	88.89
Dermatitis	53.33	91.11
Alopecia	94.44	100.00
Acne	68.75	75.00

Dermatologists

Because of the quantity of images per pathology and the total number of dermatologists involved, the evidence is inconclusive and may be biased.

Condition	Accuracy (%)	Accuracy with the device (%)
Pressure ulcer	80.00	100.00
Urticaria	80.00	100.00
Tinea	70.00	100.00
Seborrheic keratosis	50.00	66.67
Psoriasis	66.67	86.67
Onychomycosis	70.00	70.00
Nevus	66.67	80.95
Melanoma	68.75	75.00
Herpes	100.00	100.00
Granuloma annulare	66.67	100.00
Dermatitis	92.59	96.30
Alopecia	100.00	100.00
Acne	60.00	60.00

Referral

In assessing the impact of the device on referrals, our findings revealed that 58.1% of cases did not necessitate a referral. However, this percentage varied slightly to 60.89% for primary care doctors and 53.59% for dermatologists. These results are summarized in the table below:

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	168	41.9	233	58.1
Primary care	97	39.11	151	60.89
Dermatologists	71	46.41	82	53.59

Nevus

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	45	83.33	9	16.67
Primary care	27	81.82	6	18.18
Dermatologists	18	85.71	3	14.29

Melanoma

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	42	100.00	0	0.00
Primary care	26	100.00	0	0.00
Dermatologists	16	100.00	0	0.00

Alopecia

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	19	65.52	10	34.48
Primary care	9	50.00	9	50.00
Dermatologists	10	90.91	1	9.09

Urticaria

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	2	14.29	12	85.71
Primary care	0	0.00	9	100.00
Dermatologists	2	40.00	3	60.00

Granuloma annulare

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	7	46.67	8	53.33
Primary care	5	55.56	4	44.44
Dermatologists	2	33.33	4	66.67

Seborrheic keratosis

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	4	26.67	11	73.33
Primary care	2	22.22	7	77.78
Dermatologists	2	33.33	4	66.67

Herpes

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	0	0.00	28	100.00
Primary care	0	0.00	17	100.00
Dermatologists	0	0.00	11	100.00

Tinea

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	0	0.00	27	100.00
Primary care	0	0.00	17	100.00
Dermatologists	0	0.00	10	100.00

Psoriasis

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	25	62.5	15	37.5
Primary care	19	76.00	6	24.00
Dermatologists	6	40.00	9	60.00

Onychomycosis

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	4	15.38	22	84.62
Primary care	1	6.25	15	93.75
Dermatologists	3	30.00	7	70.00

Acne

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	4	15.38	22	84.62
Primary care	0	0.00	16	100.00
Dermatologists	4	40.00	6	60.00

Pressure ulcer

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	2	15.38	11	84.62
Primary care	1	12.5	7	87.50
Dermatologists	1	20.00	4	80.00

Dermatitis

HCP	Should refer	Should refer (%)	Should not refer	Should not refer (%)
All specialties	14	19.44	58	80.56
Primary care	7	15.56	38	84.44
Dermatologists	7	25.93	20	74.07

Remote consultations

Furthermore, we examined the feasibility of handling cases remotely through teledermatology. The results are presented in the subsequent table:

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	221	55.11	180	44.89
Primary care	148	59.68	100	40.32
Dermatologists	73	47.71	80	52.29

Conducting a Pearson's chi-squared test on the necessity for referrals and teleconsultations, we concluded with 95% confidence that a strong association exists between referrals and remote consultations. Specifically:

• 50.62% of cases do not require a referral and can be followed up remotely.
• 7.48% of cases do not require a referral but necessitate an in-person appointment.
• 4.49% of cases require a referral and remote consultation.
• 37.41% of cases require a referral in addition to an in-person appointment.

However, when analyzing depending on HCP speciality, we observed differing patterns. For dermatologists:

• 43.14% of cases do not require a referral and can be followed up remotely.
• 10.46% of cases do not require a referral but require an in-person appointment.
• 4.58% of cases require a referral and remote consultation.
• 41.83% of cases require a referral in addition to an in-person appointment.

In contrast, for primary care practitioners:

• 55.24% of cases do not require a referral and can be followed up remotely.
• 5.65% of cases do not require a referral but necessitate an in-person appointment.
• 4.44% of cases require a referral and remote consultation.
• 34.68% of cases require a referral in addition to an in-person appointment.

Nevus

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	7	12.96	47	87.04
Primary care	6	18.18	27	81.82
Dermatologists	1	4.76	20	95.24

Melanoma

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	1	2.38	41	97.62
Primary care	0	0.00	26	100.0
Dermatologists	1	6.25	15	93.75

Alopecia

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	15	51.72	14	48.28
Primary care	13	72.22	5	27.78
Dermatologists	2	18.18	9	81.82

Urticaria

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	11	78.57	3	21.43
Primary care	9	100.00	0	0.00
Dermatologists	2	40.00	3	60.00

Granuloma annulare

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	4	26.67	11	73.33
Primary care	3	33.33	6	66.67
Dermatologists	1	16.67	5	83.33

Seborrheic keratosis

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	9	60.00	6	40.00
Primary care	4	44.44	5	55.56
Dermatologists	4	66.67	2	33.33

Herpes

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	27	96.43	1	3.57
Primary care	16	94.12	1	5.88
Dermatologists	11	100.00	0	0.00

Tinea

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	24	88.89	3	11.11
Primary care	14	82.35	3	17.65
Dermatologists	10	100.00	0	0.00

Psoriasis

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	17	42.50	23	57.50
Primary care	8	32.00	17	68.00
Dermatologists	9	60.00	6	40.00

Onychomycosis

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	18	69.23	8	30.77
Primary care	15	93.75	1	6.25
Dermatologists	3	30.00	7	70.00

Acne

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	20	76.92	6	23.08
Primary care	13	81.25	3	18.75
Dermatologists	7	70.00	3	30.00

Pressure ulcer

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	10	76.92	3	23.08
Primary care	7	87.50	1	12.50
Dermatologists	3	60.00	2	40.00

Dermatitis

HCP	Can be handled remotely	Can be handled remotely (%)	Can't be handled remotely	Can't be handled remotely (%)
All specialties	58	80.56	14	19.44
Primary care	39	86.67	6	13.33
Dermatologists	19	70.37	8	29.63

HCP feedback

A total of 15 healthcare professionals (6 dermatologists, 6 general practitioners, and 3 family doctors) completed the Clinical Utility Questionnaire. The questionnaire included seven targeted questions addressing key aspects of device utility and clinical applicability:

Question 1: Rate or Score of Utility of Data Provided by the Device Healthcare professionals rated the utility of the data provided by the device with an average score of 7.3 out of 10. Dermatologists and general practitioners both rated it at 7.3, while family doctors rated it at 7.0. This positive feedback indicates that practitioners found the device outputs to be clinically valuable and useful for diagnostic support.

Question 2: Necessity of Additional Data When asked about the necessity of additional data beyond what the device provided, approximately three respondents indicated they did not require more information. Most respondents recognized the value of the device's current output but identified areas where complementary information would be beneficial.

Question 3: Necessity for Additional Patient History Data There was strong consensus among participants regarding the necessity for additional patient history data, particularly regarding symptoms. Practitioners emphasized that symptom descriptions and clinical history would be valuable complements to the image-based analysis, supporting more comprehensive clinical assessments.

Question 4: Consultation Time Reduction When asked about the impact on consultation time per patient—considering that when a patient uploads a photo from home and the device processes it, it counts as a consultation—practitioners reported significant time reductions:

• 67% reported managing consultations in 5 to 10 minutes using the device
• 20% reported managing consultations in under 5 minutes
• The remaining practitioners reported comparable or slightly longer consultation times

This data indicates that the device enables meaningful reductions in average consultation time, particularly for remote or home-based diagnostic consultations.

Question 5: Confidence in Remote Clinical Decisions Regarding confidence in making clinical decisions remotely using the device, the average score was 6.4 out of 10. Stratified by specialization:

• Dermatologists: 6.2
• General practitioners: 6.3
• Family doctors: 7.0

This moderate confidence score suggests that while the device provides valuable support, practitioners still rely on their clinical judgment, which is appropriate for a diagnostic decision-support tool.

Question 6: Potential Uses for the Medical Device When asked about potential uses for the medical device, practitioners identified multiple applications:

• Diagnosis support in remote consultations and video calls (most common, mentioned 12 times)
• Automatic referrals to dermatology (8 respondents)
• Remote follow-up of previously diagnosed patients (8 respondents)
• Diagnosis support in in-person consultations (7 respondents)
• Automatic triage of patients (7 respondents)

These responses demonstrate practitioners' recognition of the device's versatility and potential to integrate into various clinical workflow scenarios.

Question 7: Design and Usability of the Device The design and usability of the device received an average score of 8.0 out of 10, with strong consensus among all respondents giving it the same or very similar ratings (all between 7.5-8.5). This indicates strong satisfaction with the user interface, navigation, and overall user experience of the device.

Adverse Events and Adverse Reactions to the Product

Throughout the study, no adverse events or adverse reactions related to the investigational product have been observed. Participants have not experienced any negative reactions or side effects associated with the use of the product. This indicates a favourable safety profile of the investigational product in the context of this study.

Product Deficiencies

No deficiencies in the product have been observed during the course of this study. As a result, no corrective actions have been deemed necessary. The product has demonstrated consistent performance in accordance with the study's objectives.

Subgroup Analysis for Special Populations

In the context of the analyzed pathologies, no special population subgroups were identified for this study. The research primarily focused on the specified HCP population without subgroup differentiation.

Discussion and Overall Conclusions

Clinical Performance, Efficacy, and Safety

Summary of Performance Claims:

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Conclusions

Primary Findings: The device significantly enhanced diagnostic accuracy across all healthcare professionals, increasing it from 68.08% to 88.78% (a 20.70% absolute improvement). Primary care practitioners experienced particularly substantial improvements in diagnostic accuracy, rising from 62.90% to 89.92% (a 27.00% absolute improvement), with the integration of the device. Dermatologists similarly demonstrated improved diagnostic accuracy, increasing from 76.47% to 86.93% (a 10.50% absolute improvement) with the utilisation of the device. All improvements were statistically significant (p lower than 0.0001).

Clinical Implications: Enhanced diagnostic accuracy in primary care yields more reliable lesion identification and improved triage accuracy. Teledermatology triage significantly improves referral precision, enabling most patients to be managed in primary care while directing the most severe cases to dermatology, thereby improving patient access to appropriate specialist care (Escalé-Besa A et al. 2023, Giavina-Bianchi M et al. 2020).

Timely access to specialist care enables patients to receive correct treatment sooner, which is associated with better clinical outcomes and reduced disease burden, particularly in malignant conditions where delayed detection can lead to more advanced-stage cancers requiring substantially more intensive and costly treatment (Maul LV et al. 2024). These results underscore that prompt specialist evaluation and avoidance of diagnostic delays leads to earlier correct therapy, better patient outcomes, and markedly lower disease burden. Additionally, minimising diagnostic and referral delays leads to significant cost savings for the healthcare system by avoiding unnecessary consultations, redundant treatments, and complications associated with delayed care (Liu KJ et al. 2016).

Pathology-Level Analysis—Important Limitations: The impact of the device varied across different skin conditions, with notable improvements observed in conditions such as tinea, granuloma annulare, and seborrheic keratosis. However, some condition-specific subgroups contained fewer than 20 observations, and dermatologist-specific analyses for certain pathologies were particularly limited. Therefore, while these pathology-level findings provide preliminary insights into condition-specific performance variations, they should be interpreted with appropriate caution.

Despite these pathology-level limitations, the consistency of improvements across the majority of conditions and the large overall sample size (401 completed observations) strongly support the robustness of the primary findings at the overall and specialization-level analyses.

Referral and Remote Management: Approximately 58.1% of cases did not necessitate specialist referral, with minor differences observed between primary care practitioners (60.89%) and dermatologists (53.59%). Additionally, a substantial portion of cases (55.11%) across all specialties could be effectively managed remotely, with primary care practitioners demonstrating a slightly higher proportion suitable for remote management compared to dermatologists.

When examining pathology-level referral patterns, important nuances emerged. For conditions like melanoma and granuloma annulare, there was consensus favouring specialist referral, which appropriately reflects the clinical complexity and potential severity of these conditions. Conversely, for conditions such as acne, herpes, and tinea, practitioners generally agreed these could be effectively managed in primary care or remotely. This differentiation indicates that the device supports practitioners in making appropriate triage decisions aligned with condition severity and complexity.

Healthcare Professional Feedback and Usability: Healthcare professionals viewed the device as a useful and user-friendly tool for its intended purpose, with an average usability score of 8.0/10 and clinical utility rating of 7.3/10. All HCPs perceived the device as beneficial for diagnostic support, particularly in remote consultation settings. Notably, 87% of HCPs reported they could manage a patient in less than 10 minutes using the device, despite typical consultation times averaging at least 15 minutes. Additionally, 20% of HCPs reported managing consultations in under 5 minutes—approximately three times faster than average. This efficiency gain streamlines clinical workflows, enabling practitioners to devote more attention to complex cases and helping reduce patient waiting lists (Li X et al. 2021). By automating routine data capture and providing diagnostic support, the device allows clinicians to focus on high-value patient care rather than administrative tasks (Gomez-Cabello CA et al. 2024). Consequently, the device not only strengthens clinical decision-making but also promotes better resource allocation, decreases patient wait times, and may lower per-patient costs for the healthcare system.

Overall Conclusion: This study demonstrates that Legit.Health Plus has meaningful clinical utility as a diagnostic support tool, particularly for primary care practitioners managing dermatological conditions. The substantial improvements in overall diagnostic accuracy, combined with significant efficiency gains and positive user feedback, suggest meaningful clinical value. The device appears to address a critical gap in dermatological expertise availability, particularly in primary care settings where diagnostic uncertainty is common. By enhancing diagnostic accuracy and enabling appropriate triage and remote management, the device can support more efficient patient care pathways and improve resource allocation within healthcare systems.

In this experiment, as reported above, we found out that according to the primary HCP with the information provided by the device 39.11% of the cases should be referred, which is a 41.34% lower than the aforementioned referral rates. In addition, our results improve the remote consultation rates, which suggests that diagnosis support tools can help foster remote consultations. In this way, the implementation of teledermatology and reduction of referrals also lead to cost savings, with some studies suggesting estimates ranging from 20.28$to 35.68$ per patient (Liu et al., 2016). Additionally, teledermatology also improves access to skin cancer screening for underserved populations (Naka et al., 2018). Overall, these approaches optimise healthcare resource utilisation and improve patients' outcomes in primary care settings.

References

1. Escalé-Besa A, Yélamos O, Vidal-Alaball J, et al. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Sci Rep 2023;13(1): 4293. doi: 10.1038/s41598-023-31340-1. (https://doi.org/10.1038/s41598-023-31340-1).

2. Giavina-Bianchi M, Santos AP, Cordioli E. Teledermatology reduces dermatology referrals and improves access to specialists. EClinicalMedicine 2020; 29-30. doi: 10.1016/j.eclinm.2020.100641. (https://doi.org/10.1016/j.eclinm.2020.100641).

3. Maul LV, Jamiolkowski D, Lapides RA, et al. Health Economic Consequences Associated With COVID-19-Related Delay in Melanoma Diagnosis in Europe. AMA Netw Open 2024;7(2):2356479. doi: 10.1001/jamanetworkopen.2023.56479. (https://doi.org/10.1001/jamanetworkopen.2023.56479).

4. Liu KJ, Hartman RI, Joyce C, et al. Modeling the Effect of Shared Care to Optimize Acne Referrals From Primary Care Clinicians to Dermatologists. JAMA Dermatol 2016;152(6):655-660. doi: 10.1001/jamadermatol.2016.0183. (https://doi.org/10.1001/jamadermatol.2016.0183).

5. Li X, Tian D, Li W, Dong B, et al. (2021). Artificial intelligence-assisted reduction in patients' waiting time for outpatient process: a retrospective cohort study. BMC Health Services Research, 21:237. doi: 0.1186/s12913-021-06248-z. (https://doi.org/10.1186/s12913-021-06248-z).

6. Gomez-Cabello, C.A., Borna S, Pressman S, et al. (2024). Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations. European Journal of Investigation in Health, Psychology and Education, 14, 685-698. doi: 10.3390/ejihpe14030045. (https://doi.org/10.3390/ejihpe14030045).

7. González-López G, Descalzo-Gallego MÁ, Arias-Santiago S, et al. Derivación de pacientes en consulta de dermatología y de teledermatología en España. Estudio DIADERM. Actas Dermo-Sifiliográficas. 2019 Mar 1;110(2):146-52. doi: 10.1016/j.ad.2018.09.004. (https://doi.org/10.1016/j.ad.2018.09.004).

8. Pagani K, Lukac D, Olbricht SM, et al. Urgent referrals from primary care to dermatology for lesions suspicious for skin cancer: patterns, outcomes, and need for systems improvement. Archives of dermatological research. 2023 Jul;315(5):1397-400. doi: 10.1007/s00403-022-02456-7. (https://doi.org/10.1007/s00403-022-02456-7).

9. Ludwick DA, Lortie C, Doucette J, et al. Evaluation of a telehealth clinic as a means to facilitate dermatologic consultation: pilot project to assess the efficiency and experience of teledermatology used in a primary care network. J Cutan Med Surg. 2010 Jan-Feb;14(1):7-12. doi: 10.2310/7750.2010.09012. (https://doi.org/10.2310/7750.2010.09012).

10. Zakaria A, Maurer T, Su G, Amerson E. Impact of teledermatology on the accessibility and efficiency of dermatology care in an urban safety-net hospital: A pre-post analysis. J Am Acad Dermatol. 2019 Dec;81(6):1446-1452. doi: 10.1016/j.jaad.2019.08.016. (https://doi.org/10.1016/j.jaad.2019.08.016).

11. Finnane A, Dallest K, Janda M, Soyer HP. Teledermatology for the Diagnosis and Management of Skin Cancer: A Systematic Review. JAMA Dermatol. 2017 Mar 1;153(3):319-327. doi: 10.1001/jamadermatol.2016.4361. (https://doi.org/10.1001/jamadermatol.2016.4361).

12. Naka F, Lu J, Porto A, et al. Impact of dermatology eConsults on access to care and skin cancer screening in underserved populations: A model for teledermatology services in community health centers. J Am Acad Dermatol. 2018 Feb;78(2):293-302. doi: 10.1016/j.jaad.2017.09.017. (https://doi.org/10.1016/j.jaad.2017.09.017).

Implications for Future Research

The positive outcomes of this study pave the way for several avenues of future research. Firstly, helping to improve the diagnosis of different skin pathologies, which can significantly impact the quality of life of patients who suffer from them, especially those affected by rare skin conditions.

On the other hand, exploring the integration of artificial intelligence and machine learning techniques to refine the tool's diagnostic capabilities warrants attention. This could lead to even more accurate and reliable assessments, potentially revolutionising the field of dermatology.

Additionally, conducting long-term studies to evaluate the impact of the device on patient outcomes, including treatment adherence and quality of life, would provide a comprehensive understanding of its broader clinical implications.

Limitations of Clinical Research

The main limitations of the pilot include several factors that may influence the perception and effectiveness of the AI-based device. Firstly, the acceptance and trust of healthcare professionals in these emerging technologies can vary significantly. The device's effectiveness may be compromised if users are not fully convinced of its accuracy or usefulness, thereby affecting the overall perception of its performance.

Additionally, image quality is crucial for the device's performance. Issues such as low-quality photographs, errors in cropping lesions, or variations in lighting and focus can deteriorate the quality of the data received by the system, which may negatively influence the evaluation and perception of its effectiveness by the researchers.

Variability in image conditions is also an important aspect to consider. Differences in lighting, colour, shape, size, and focus of the images, along with the number of images available for each patient, can affect the accuracy of the results. High variability in images of the same patient or an insufficient number of representative images can decrease the expected diagnostic accuracy of the device.

Additionally, the consistency of investigators in using the device is crucial. Variations in how diligently investigators use the device can impact the pilot's findings. If the investigators are not consistent in their use of the device, it can lead to unreliable results and affect the overall assessment of its efficacy.

Another limitation is the Hawthorne effect, where pilot subjects may change their behaviour simply because they know they are being observed. This awareness can influence their decisions and actions within the pilot, potentially skewing the results and not accurately reflecting how the device would be used in a non-study environment.

Ethical Aspects of Clinical Research

This study adhered to international Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki in its latest amendment, and applicable international and national regulations. As applicable, approval from the relevant Ethics Committee was obtained prior to the initiation of the study. When applicable, modifications to the protocol were reviewed and approved by the Principal Investigator (PI) and subsequently evaluated by the Ethics Committee before subjects were enrolled under a modified protocol.

This study was conducted in compliance with European Regulation 2016/679, of 27 April, concerning the protection of natural persons with regard to the processing of personal data and the free movement of such data (General Data Protection Regulation, GDPR), and Organic Law 3/2018, of 5 December, on the Protection of Personal Data and the guarantee of digital rights. In accordance with these regulations, no data enabling the personal identification of participants was collected, and all information was managed securely in an encrypted format.

Participants were informed both orally and in writing about all relevant aspects of the study, with the information being tailored to their level of understanding. They were provided with a copy of the informed consent form and the accompanying patient information sheet. Adequate time was given to patients to ask questions and fully comprehend the details of the study before providing their consent.

The PI was responsible for the preparation of the informed consent form, ensuring it included all elements required by the International Conference on Harmonisation (ICH), adhered to current regulatory guidelines, and complied with the ethical principles of GCP and the Declaration of Helsinki.

The original signed informed consent forms were securely stored in a restricted access area under the custody of the PI. These documents remained at the research site at all times. Participants were provided with a copy of their signed consent form for their records.

Data quality assurance

The Principal Investigator is responsible for reviewing and approving the protocol, signing the Principal Investigator commitment, guaranteeing that the persons involved in the centre will respect the confidentiality of patient information and protect personal data, and reviewing and approving the final study report together with the sponsor. All the clinical members of the research team assess the eligibility of the patients in the study, inform and request written informed consent, collect the source data of the study in the clinical record and transfer them to the Data Collection Notebook (DCN) or Data Collection Forms (CRF).

Investigators and Administrative Structure of Clinical Research

Brief Description

This CIP has been conducted between the participating medical staff and AI Labs Group S.L. (the manufacturer).

Investigators

Principal investigator

• Dr Antonio Martorell Calatayud

Collaborators

• Medical staff
  • Dr Adriana Vasconcelos
  • Dr María Porriño
  • Dr Gustazo
  • Dr Josefina Sanz
  • Dr Andrés
  • Dr Gerald Selda
  • Dr Helena Bahachille
  • Dr Mitchell Ignacio Leal Betancourt
  • Dr Marianela del Castillo
  • Dr María Pilar Martínez Marta
  • Dr Nadia Hayajneh Carrillo
  • Dr Carmen Arsuaga
  • Dr Elena Sánchez Largo
  • Dr María Gómez
  • Dr Pedro Ortega Lozano
• AI Labs Group S.L.
  • Mr Alfonso Medela
  • Mr Taig Mac Carthy

External Organization

No additional organizations, beyond those previously mentioned, contributed to the clinical research. The study was conducted with the collaboration and resources of the specified entities.

Sponsor and Monitor

Sanitas Hospitales SA

Report Annexes

• Instructions For Use (IFU) can be found in the protocol.

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