Evaluating an Artificial Intelligence Tool to Help Primary Care Doctors Diagnose Skin Conditions. (LegitHealth PH)

February 18, 2026 updated by: AI Labs Group S.L

A Multi-Reader Multi-Case (MRMC) Study Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for Skin Lesions.

This study aims to determine if an artificial intelligence (AI) medical device can help primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to misdiagnoses or unnecessary referrals.

The researchers hypothesized that the information provided by the AI device would increase the true diagnostic accuracy of primary care practitioners for multiple dermatological conditions. To test this, the study followed a prospective, self-controlled design where each participating doctor served as their own comparison.

During the study, 9 primary care physicians evaluated 30 clinical images representing a variety of skin pathologies. For each image, the doctors followed a two-step process:

  • First, they provided a diagnosis based only on the image and the patient's medical history.
  • Second, they were shown the AI's analysis-including the top 5 suggested diagnoses and confidence levels-and asked to provide a final diagnosis.

The study also investigated if the AI could help doctors decide whether a patient truly needs a referral to a specialist or if the condition could be handled remotely via teledermatology. The primary question was whether using this AI support would significantly increase the number of correct diagnoses made by primary care doctors and lead to more efficient patient care.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

This detailed description outlines the clinical methodology, technical framework, and data integrity protocols utilized in the investigation of the Legit Health Plus medical device for skin pathologies in primary care.

Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional self-controlled study. It utilized a Multi-Reader Multi-Case (MRMC) framework to measure the impact of Computer-Aided Diagnosis (CAD) on clinician performance.

  • Self-Controlled Framework: Each primary care practitioner (PCP) served as their own comparator, providing diagnoses first without and then with device support.
  • Sequential Evaluation Workflow: Participants accessed a secure web-based platform to review 30 clinical cases. For each case, doctors provided an initial diagnosis based on an image and medical history, followed by a final diagnosis after reviewing the AI's top 5 suggested ICD-11 categories and confidence levels.
  • Clinical Decision Support: The study also evaluated clinician decisions regarding dermatology referrals and the feasibility of remote management (teledermatology) based on AI-provided data, such as malignancy indices.
  • Case Distribution: The 30 clinical images represented nine different conditions, including Melanoma, Basal Cell Carcinoma, Psoriasis, and Hidradenitis Suppurativa, all previously confirmed by dermatologists and anatomical pathology.

Quality Assurance and Data Management

To ensure the scientific integrity of the clinical investigation, the following quality and monitoring protocols were implemented:

  • Centralized Case Report Forms (CRF): All data were collected via a secure web platform where entries were time-stamped and stored in a central database.
  • Data Validation and Checks: Validation rules were applied using computer filters to automatically identify missing values or logical inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect errors.
  • Monitoring Plan: An independent clinical monitor oversaw the investigation. Monitoring included remote video or telephone meetings every three months to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155:2020.
  • Bias Minimization: Random selection of practitioners helped ensure that outcomes were not influenced by pre-existing group characteristics. Standardized protocols ensured all participants were evaluated under identical conditions.

Study Type

Observational

Enrollment (Actual)

9

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Spain
    • Basque Country
      • Bilbao, Basque Country, Spain, 48001
        • AI Labs Group S.L.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population consists of board-certified healthcare professionals recruited from the clinical fields of general medicine. The participant group includes:

  • Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms.
  • Experience Level: The cohort includes practitioners with at least 5 years of clinical experience in their respective specialities.

Participants were recruited to engage in a remote, web-based evaluation environment rather than being selected from a single physical hospital or town. The clinical images evaluated as part of the study "cases" were sourced from international public dermatology atlases and existing research databases from the sponsor, representing a diverse global patient population.

Description

Inclusion Criteria:

  • Board-certified primary care physicians 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Primary Care Physicians

This group is composed of board-certified healthcare professionals (HCPs) who serve as the "readers" in this multi-reader multi-case (MRMC) study. The cohort is uniquely characterized by its internal comparison: each participant acts as their own control.

  • The group includes 9 primary care physicians (PCPs), allowing for a comparison of PCPs diagnostic baseline performance.
  • Interventional Exposure: All participants are evaluated under two distinct conditions: first, providing a diagnosis based solely on clinical images and patient history; second, providing a diagnosis assisted by the AI-based medical device's top 5 suggestions and confidence levels.
  • Clinical Expertise: Every member of the cohort has a minimum of 5 years of clinical experience in their respective field.
Device: AI-based medical device for aided diagnosis in dermatological conditions
The intervention consists of a Computer-Aided Diagnosis (CAD) software-only medical device that utilizes computer vision algorithms to analyze digital images of skin structures. During the study, healthcare professionals use the tool as a diagnostic support system to assist in the evaluation of complex dermatological conditions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
Time Frame: Day 1
This measure evaluates the "Top-1" diagnostic accuracy of primary care practitioners (PCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI's top 5 suggestions-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology).
Day 1

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Dermatology Referral Rate Assisted by Artificial Intelligence.
Time Frame: Day 1
This outcome validates the percentage of cases that practitioners determine should be referred to a dermatology specialist after reviewing the AI-provided information, including malignancy indices and tool recommendations. The goal is to evaluate if the device helps optimize resource allocation by reducing unnecessary referrals
Day 1
Percentage of Cases Deemed Manageable via Remote Consultation.
Time Frame: Day 1
This measure assesses the practitioners' evaluation of whether a case can be confirmed and treated remotely through teledermatology based on the AI analysis. A Pearson's chi-squared test is used to analyze the association between referral necessity and remote consultation feasibility.
Day 1

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

AI Labs Group S.L

Collaborators

Puerta de Hierro University Hospital

Investigators

  • Principal Investigator: Antonio Martorell, PhD, Hospital Universitari de Manises

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 4, 2024

Primary Completion (Actual)

September 13, 2024

Study Completion (Actual)

September 13, 2024

Study Registration Dates

First Submitted

February 18, 2026

First Submitted That Met QC Criteria

February 18, 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 18, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Legit.Health_PH_2024
  • EUPAS1000000644 (Other Identifier: European Medicines Agency)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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