An observational study in Skin Lesions, Melanocytic Nevi and Melanoma (Skin Cancer), sponsored by AI Labs Group S.L. Completed at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-02-24.
Sponsored by AI Labs Group S.L · Observational
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:
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.
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.
Quality Assurance and Data Management
To ensure the scientific integrity of the clinical investigation, the following quality and monitoring protocols were implemented:
19 studies on the registry are indexed under Nevus, Pigmented; 6 are open to participants now.
This study's enrollment of 9 is below the median of 50 across 11 observational studies indexed under Nevus, Pigmented.
Browse Nevus, Pigmented studies →AI Labs Group S.L is the lead sponsor of 8 studies on the registry; 1 is open to participants now.
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The study population consists of board-certified healthcare professionals recruited from the clinical fields of general medicine. The participant group includes:
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.
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Exclusion Criteria:
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.
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
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).
Time frame: Day 1
Change in Dermatology Referral Rate Assisted by Artificial Intelligence.
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
Time frame: Day 1
Percentage of Cases Deemed Manageable via Remote Consultation.
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.
Time frame: Day 1
Plan to share: No
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This study is completed, as verified in Feb 2026. You cannot join it, but the record below documents what was studied.
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AI Labs Group S.L