Evaluating Legit.Health Plus Support for Improving Diagnosis of Generalized Pustular Psoriasis and Other Skin Conditions Among Primary Care Physicians and Dermatologists (LegitHealth BI)

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

A Multi-Reader Multi-Case (MRMC) Study for Assessing the Impact of Legit.Health Plus on the Clinical Assessment of Generalized Pustular Psoriasis and Other Skin Conditions by Healthcare Professionals.

This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists, which can lead to treatment delays for serious conditions like Generalized Pustular Psoriasis (GPP) and Hidradenitis Suppurativa (HS).

The researchers hypothesized that the AI device would enhance the diagnostic accuracy of healthcare professionals for GPP and other dermatological conditions. To test this, the study followed a prospective observational design involving 15 practitioners, including both general practitioners and dermatologists.

During the study, participants were asked to evaluate 100 clinical images. For each case, they first provided a diagnosis based on the image and patient history alone. They were then shown the AI's analysis-which included the top five suggested diagnoses and confidence levels-and asked if they would like to adjust their initial assessment.

The primary question the study sought to answer was whether the information provided by the AI device could significantly increase the number of correct diagnoses made by these professionals, particularly for rare diseases that are often difficult to identify in a standard clinical setting

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

This investigation is structured as a multi-reader multi-case (MRMC) study. A cohort of 15 healthcare professionals, including 11 primary care physicians and 4 dermatologists, acted as the "readers". These readers evaluated a "case" set of 100 clinical images to assess diagnostic performance both with and without the assistance of the AI device.

Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional study. It utilized a "physician-as-their-own-control" design to measure the impact of Artificial Intelligence (AI) on diagnostic performance.

  • Intervention Workflow: Participants accessed a dedicated web platform where they were presented with 100 clinical cases.
  • Evaluation Steps: For each case, practitioners first evaluated a clinical image alongside anamnesis data (e.g., allergies, systemic symptoms) to provide an initial diagnosis.
  • AI Support: Subsequently, they were presented with the AI's top 5 suggested diagnoses and associated confidence levels before making a final assessment.
  • Image Sourcing: Cases consisted of high-quality images of Generalized Pustular Psoriasis (GPP), Hidradenitis Suppurativa (HS), and various differential "look-alike" conditions such as subcorneal pustular dermatosis and palmoplantar pustulosis.
  • Data Sources: These images were curated from public dermatology atlases and internal research databases from the sponsor.

Quality Assurance and Data Management

To ensure the scientific integrity and reliability of the findings, several quality control measures were implemented:

  • Data Validation: A validation process of the clinical data was carried out by running computer filters based on validation rules.
  • Error Detection: These filters automatically identify missing values or inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect logical errors and inconsistencies.
  • Monitoring Plan: The investigation was overseen by a designated clinical monitor appointed by the sponsor.
  • Remote Supervision: Monitoring activities included remote meetings every three months to review study progress and ensure ongoing compliance.
  • Bias Minimization: Random selection of healthcare professionals (HCPs) was used to ensure that outcomes were not influenced by pre-existing participant characteristics.
  • Standardization: The use of standardized procedures ensured that all participants were evaluated in the same way.

Statistical Analysis Plan

The primary goal of the analysis was to quantify Top-1 accuracy, sensitivity, and specificity for both general practitioners and dermatologists.

  • Comparative Metrics: The analysis focused on the absolute metric values compared against the state of the art, as well as the percentage of variation attributable specifically to the use of the device.
  • Subgroup Analysis: A dedicated analysis was performed for rare diseases, including GPP, Acne Conglobata, and Pemphigus Vulgaris, to assess the device's utility in high-complexity, low-incidence conditions.

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

  • Anonymization: No data enabling the personal identification of participants or patients was collected.
  • Encryption: All information was managed securely in an encrypted format.
  • End of Study: All information stored in the device will be totally and permanently deleted at the end of the study.

Study Type

Observational

Enrollment (Actual)

15

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 general practitioners and dermatologists, regardless of their professional experience.
  • Good quality images of patients with GPP.
  • Good quality images of patients with HS.
  • Good quality images of patients with pathologies that can be confused with GPP or HS, leading to a wrong diagnosis.

Exclusion Criteria:

  • Images of patients with pathologies different from GPP or HS that can be easily identified.

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 11 primary care physicians (PCPs) and 4 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 Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.
Time Frame: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying GPP. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Day 1
Diagnostic Accuracy for different skin conditions with and without Artificial Intelligence Support
Time Frame: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying the corresponding skin condition. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Day 1

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic Accuracy for Rare Dermatological Conditions with and without Artificial Intelligence Support.
Time Frame: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying rare dermatological conditions. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
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)

September 15, 2024

Study Completion (Actual)

September 15, 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_BI_2024
  • EUPAS1000000910 (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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