Using Artificial Intelligence to Help Doctors Identify Different Skin Conditions and Improve Patient Care (LegitHealthSAN)

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

A Multi-Reader Multi-Case Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions

This study aims to determine if an artificial intelligence (AI) medical device can help doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. While many patients visit primary care for skin issues, general doctors may sometimes have different opinions from specialists, which can lead to delays in getting the right treatment.

The researchers hypothesized that using the AI tool would increase the true diagnostic accuracy of healthcare professionals for multiple skin conditions. To test this, 16 doctors (including 10 general practitioners and 6 dermatologists) evaluated 29 different medical images.

For each case, the doctors followed a structured process:

  • Initial Assessment: Doctors first gave a diagnosis based only on the patient's image and medical history.
  • AI Support: Doctors were then shown the AI's top five suggested diagnoses and confidence levels to see if they wished to adjust their final decision.
  • Clinical Utility: Doctors also indicated if the patient required a specialist referral and if the case could be handled through a remote (online) consultation.

The primary question the study tried to answer was whether AI support could significantly improve correct diagnoses across 13 different types of skin pathologies-ranging from common rashes to skin cancer-while also making the consultation process faster and more effective for both doctors and patients.

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 and dermatology.

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 evaluate the impact of Computer-Aided Diagnosis (CAD) on clinician performance.

  • Self-Controlled Framework: Each healthcare professional (HCP) served as their own comparator, providing diagnoses first without the use of the device and subsequently with the support of the device on the same set of images.
  • Evaluation Workflow: Participants accessed a secure web-based platform to review 29 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 International Classification of Diseases (ICD) categories and confidence levels.
  • Clinical Utility Assessment: The study included a specific questionnaire to evaluate the utility of the data, consultation time reductions, and confidence in making remote clinical decisions.
  • Pathology Diversity: The dataset included 13 distinct skin conditions, ranging from common ailments like Acne and Dermatitis to malignant conditions such as Melanoma and Basal Cell Carcinoma.

Quality Assurance and Data Management

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

  • Data Validation and Checks: A validation process was carried out by running computer filters based on validation rules to automatically identify missing values or inconsistencies. Manual editing and exploratory statistical techniques were also used to complement error detection.
  • Monitoring Plan: The investigation was monitored by a designated clinical monitor, independent of the investigational site, to ensure the protection of subject rights and data accuracy. Monitoring included remote video or telephone meetings every 3 months.
  • Bias Minimization: HCPs were randomly selected to ensure that outcomes were not influenced by pre-existing participant characteristics. Standardized procedures for conducting the study and measuring outcomes were strictly followed to reduce variability.

Ethical and Confidentiality Framework The study adhered to ISO 14155:2021, the Declaration of Helsinki, and the General Data Protection Regulation (GDPR).

  • Anonymization: All clinical images were completely anonymized and sourced from public dermatological atlases, containing no information that would allow the identification of patients.
  • Data Security: All data entries were timestamped, encrypted using industry-standard security protocols, and stored in a secure central database.
  • Data Retention: Upon completion of the study and drafting of the final report, all information stored in the device platform is scheduled to be permanently deleted.

Study Type

Observational

Enrollment (Actual)

16

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
        • 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 and dermatology. The participant group includes:

  • Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms.
  • Specialist Dermatologists: Physicians with advanced expertise in skin pathologies and rare conditions.
  • 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 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.

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
Healthcare Professionals (Primary Care Physicians and Dermatologists)
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. - Dual Professional Roles: The group includes 10 primary care physicians (PCPs) and 6 dermatologists, allowing for a comparison between generalist and specialist 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 Dermatology
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 healthcare professionals (HCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI device's top 5 suggestions and confidence levels-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, which includes malignancy indices and referral recommendations.
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.
Day 1
Clinical Utility and Usability Scores for Diagnostic Support.
Time Frame: Day 1
This outcome assesses the perceived value of the device using a Clinical Utility Questionnaire. It measures average utility of data (on a scale of 0-10, where 10 is most useful), system usability scores, and the impact on consultation time reduction.
Day 1

Collaborators and Investigators

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

Sponsor

AI Labs Group S.L

Investigators

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

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

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 1, 2024

Primary Completion (Actual)

October 10, 2024

Study Completion (Actual)

October 10, 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_SAN_2024
  • EUPAS1000000911 (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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