Using an Artificial Intelligence Medical Device to Help Primary Care Practitioners Identify and Manage Skin Conditions (Tumor, Inflammatory, and Infectious Diseases) in Adult Patients at Pozuelo and Majadahonda Health Centers

A Project to Enhance Dermatology E-consultations in Primary Care Centers Using Artificial Intelligence Tools.

The goal of this observational study is to learn if an artificial intelligence (AI) tool helps primary care practitioners better identify skin conditions. The study focuses on adults with suspected skin pathologies, including tumor, inflammatory, and infectious diseases.

The main questions it aims to answer are:

• Does using the AI tool help doctors make more accurate diagnoses for multiple skin conditions?
• Does the tool help doctors better decide which patients need a referral to a dermatologist and which can be managed in primary care?
• Are doctors satisfied with how well the tool works and how easy it is to use in their daily work?
• Can the tool help doctors more accurately differentiate between benign lesions and skin cancer?

Participants will:

• Visit their primary care doctor for a regular skin checkup.
• Have photos taken of their skin condition using a smartphone or a dermatoscope.
• Provide informed consent for their photos and basic health information (such as age and sex) to be analyzed by the AI tool.
• Receive standard care from their doctor, with the tool providing a second opinion to assist in the clinical decision-making process.

Study Overview

• Status: Completed
• Conditions: Skin Abnormalities; Skin Cancer; Skin Condition; Malignancy; Melanoma (Skin Cancer)
• Intervention / Treatment: Device: Primary care practitioners aided by Legit.Health Plus

Detailed Description

This prospective, observational study evaluates the clinical utility of an artificial intelligence (AI)-based computational software device designed to support primary care practitioners (PCPs) and dermatologists in managing skin pathologies. The research explores whether the device can enhance diagnostic accuracy and optimize the referral process from primary care to specialized dermatology services.

Study Methodology and Design

The investigation is designed as an analytical study of a clinical case series. Key technical aspects include:

• Investigational Tool: A software-only medical device using computer vision algorithms to analyze images of the epidermis and dermis to provide clinical data for assessment.
• Participant Roles: 15 HCPs (including PCPs and dermatologists) evaluated with a cohort of over 100 patients.
• Procedural Workflow: PCPs captured skin images using smartphones or mobile dermatoscopes, uploaded them to the platform, and provided a diagnosis guided by the AI results.
• Evaluation Baseline: HCPs acted as their own controls, allowing for a comparison of diagnostic performance with and without the AI tool.

Quality Assurance and Registry Procedures

To ensure the integrity of the data collected within this organized system, several quality control measures were implemented:

• Data Validation and Checks: The database utilized consistency rules and logical ranges to control errors during tabulation. Computerized validation filters automatically identified missing values or inconsistencies based on predefined rules.
• Source Data Verification (SDV): A designated independent clinical monitor performed verification of anonymized source documents (e.g., image files and clinical records) against Case Report Forms (CRFs) to ensure accuracy and completeness.
• Monitoring Plan: The research team held quarterly meetings to address data collection issues, while the monitor conducted remote and, if necessary, on-site visits to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155 standards.
• Missing Data Management: Manual editing and exploratory statistical techniques were used to detect and resolve logical errors or inconsistent values before the database was considered closed.

Sample Size and Statistical Principles The study was powered to detect a 10% improvement in diagnostic accuracy.

• Assessment Power: A sample size of 100 patients was determined to provide a 95% confidence level with an 80% power and a margin of error between 9% and 10%.
• Analytical Techniques: Central tendency and variability statistics (mean, SD) were used for quantitative variables, while qualitative variables were analyzed through frequency distributions.
• In addition to parametric tests, the McNemar test was used to analyze the specific impact of AI on HCP diagnostic choices. Statistical significance was set at alpha = 0.05.

For qualitative data, Fisher's exact or Chi-square tests were employed. Statistical significance was set at alpha = 0.05.

Safety and Ethical Standards The study complied with Regulation (EU) 2017/745 (MDR) and ISO 14155:2021. Data protection followed GDPR and Spanish Organic Law 3/2018, utilizing encrypted patient information and alphanumeric identification codes to maintain participant anonymity. All clinical data stored on the device is permanently deleted upon study conclusion.

• Study Type: Observational
• Enrollment (Actual): 131

Contacts and Locations

Study Locations

• Spain
  • Madrid
    • Majadahonda, Madrid, Spain
      • Puerta de Hierro Majadahonda University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population is drawn from adult patients presenting with dermatological concerns at two primary care centers in the Madrid region: Centro de Salud de Majadahonda and Centro de Salud de Pozuelo. These participants are residents within the catchment areas of these clinics, with Hospital Universitario Puerta de Hierro Majadahonda serving as their reference hospital for specialized dermatology care.

The source population consists of individuals in a real-world clinical setting undergoing preliminary assessment by primary care practitioners (PCPs) for potential referral to specialized dermatology services. This diverse cohort is intended to represent a typical population affected by various skin pathologies, specifically including tumoral (benign and malignant), inflammatory, and infectious conditions.

Participants are identified and recruited during routine medical visits whenever a skin-related pathology is suspected.

Description

Inclusion Criteria:

• Tumor pathology:
• Benign:
• Histiocytoma
• Seborrheic keratosis
• Angiomas
• Precancerous:
• Actinic keratosis
• Suspected malignancy:
• Basal cell carcinoma
• Squamous cell carcinoma
• Pigmented lesions:
• Melanocytic nevus
• Malignant melanoma
• Inflammatory pathology:
• Psoriasis
• Atopic dermatitis
• Urticaria
• Hidradenitis suppurativa
• Lichen planus
• Infectious pathology:
• Viral warts
• Molluscs
• Herpes simplex
• Patients aged 18 years or older.
• Patients who have signed the informed consent for the study.

Exclusion Criteria:

• Patients under 18 years of age.
• Pregnant patients.
• Patients who, in the opinion of the researcher, will not comply with the study procedures.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Patiens with skin conditions treated in Primary care; This single-group cohort comprises adult patients presenting with diverse skin pathologies who were evaluated by healthcare professionals (HCPs) using an AI-based clinical decision support tool. The cohort includes individuals suspected of having tumoral (benign or malignant), inflammatory, or infectious conditions.

Device: Primary care practitioners aided by Legit.Health Plus; The device is a computer vision software designed to assist healthcare practitioners in assessing skin structures through the analysis of digital images. Primary care practitioners utilize the device by capturing photographs of affected skin areas with a smartphone or mobile dermatoscope and uploading them to the platform. The software processes images of the epidermis and dermis to quantify visible clinical signs-including intensity, count, and extent-and provides an interpretive distribution of possible International Classification of Diseases (ICD) categories. Practitioners use the platform's results as a second medical opinion to guide diagnosis, triage, and referral decisions for pathologies including tumoral (benign and malignant), inflammatory, and infectious conditions. The intervention also provides clinicians with access to specific referral criteria, clinical questionnaires, and basic treatment

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Referral appropriateness	This metric evaluates the appropriateness of patient referrals from primary care to specialized dermatology services. A referral is classified as "avoidable" or "unnecessary" when both the primary care practitioner and the expert dermatologist agree that the case can be effectively managed within primary care without a specialist consultation. The study's primary target for this metric was a minimum increase in referral adequacy of 15%. This threshold represents the minimum clinically important difference required to demonstrate the device's utility in optimizing clinical workflows and reducing healthcare costs.	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area Under the ROC Curve (AUC) for Malignancy Detection	This measure utilizes the Area Under the ROC Curve (AUC) to evaluate the device's discriminatory performance in differentiating between malignant lesions (including melanoma and basal cell carcinoma) and benign lesions. The AUC provides a comprehensive assessment of the tool's ability to correctly classify skin pathologies across various decision thresholds. The predefined acceptance criterion for this metric was an AUC $\ge$ 80%. This threshold ensures that the device provides clinically meaningful support in identifying high-risk cases that require urgent specialist intervention.	Baseline
Healthcare Professional Satisfaction (CUS Score)	Satisfaction is evaluated using the Clinical Utility and Satisfaction (CUS) Questionnaire. This validated assessment tool measures practitioners' perspectives on the device's diagnostic support, ease of use, data utility, and overall clinical applicability within their workflow. Results from the questionnaire are quantified either as a percentage of affirmative responses or as a mean score on a 10-point scale. This dual approach allows for both a qualitative understanding of practitioner consensus and a quantitative measure of perceived value.	4 months (practitioners completed the questionnaire twice: once at 2 months and again at 4 months after starting the study).

Collaborators and Investigators

• Sponsor: AI Labs Group S.L
• Collaborators: Puerta de Hierro University Hospital; Fundación para la Investigación Biomédica, Hospital Universitario Puerta de Hierro
• Investigators: Principal Investigator: Gaston Roustan, PhD, Puerta de Hierro Majadahonda University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): June 24, 2022
• Primary Completion (Actual): December 19, 2023
• Study Completion (Actual): January 10, 2024

Study Registration Dates

• First Submitted: February 10, 2026
• First Submitted That Met QC Criteria: February 17, 2026
• First Posted (Actual): February 24, 2026

Study Record Updates

• Last Update Posted (Actual): February 24, 2026
• Last Update Submitted That Met QC Criteria: February 17, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Primary care; Melanoma; Skin cancer; Triage; Diagnostic accuracy; Dermatolgy
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms by Histologic Type; Congenital Abnormalities; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Neuroendocrine Tumors; Nevi and Melanomas; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Skin and Connective Tissue Diseases; Neoplasms; Melanoma; Skin Neoplasms; Skin Diseases; Skin Abnormalities
• Other Study ID Numbers: LEGIT.HEALTH_DAO_Derivación_PH

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No