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

NCT07428915 | Completed

• 2 other identifiers: Legit.Health_BI_2024EUPAS1000000910
• Study Type: observational
• Target: 15
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Pustular Psoriasis (PP); Eczema Atopic Dermatitis; Acute Generalized Exanthematous Pustulosis; Acne; Acne Conglobata; Seborrheic Keratoses; Seborrheic Dermatitis; Palmoplantar Pustulosis; Plaque Psoriasis; Pemphigus Vulgaris (PV); Impetigo; Hidradenitis Suppurativa (HS); Tinea Corporis; Generalized Pustular Psoriasis (GPP)

Keywords

Generalized Pustular Psoriasis; Dermatologists; Primary Care Physicians; Rare diseases; Skin conditions

Outcome Measures

Primary Outcomes (2)

• Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.

  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

  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 Outcomes (1)

• Diagnostic Accuracy for Rare Dermatological Conditions with and without Artificial Intelligence Support.

  Day 1

Study Arms (1)

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

Interventions

AI-based medical device for aided diagnosis in Dermatology DEVICE

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.

Healthcare Professionals (Primary Care Physicians and Dermatologists)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• AI Labs Group S.L: lead

Study Sites (1)

AI Labs Group S.L.

Bilbao, Basque Country, Spain

Location

Related Links

• Study website on RWD catalogues of EMA

MeSH Terms

Conditions

Psoriasis; Acute Generalized Exanthematous Pustulosis; Acne Vulgaris; Acne Conglobata; Keratosis, Seborrheic; Dermatitis, Seborrheic; Pemphigus; Impetigo; Hidradenitis Suppurativa; Tinea; Rare Diseases; Skin Diseases

Condition Hierarchy (Ancestors)

Skin Diseases, Papulosquamous; Skin and Connective Tissue Diseases; Drug Eruptions; Dermatitis; Hypersensitivity, Delayed; Hypersensitivity; Immune System Diseases; Drug Hypersensitivity; Drug-Related Side Effects and Adverse Reactions; Chemically-Induced Disorders; Acneiform Eruptions; Sebaceous Gland Diseases; Keratosis; Skin Diseases, Eczematous; Skin Diseases, Vesiculobullous; Autoimmune Diseases; Staphylococcal Skin Infections; Staphylococcal Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Streptococcal Infections; Skin Diseases, Bacterial; Skin Diseases, Infectious; Suppuration; Hidradenitis; Sweat Gland Diseases; Dermatomycoses; Mycoses; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Antonio Martorell, PhD

  Hospital Universitari de Manises

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 18, 2026
• First Posted: February 24, 2026
• Study Start: June 1, 2024
• Primary Completion: September 15, 2024
• Study Completion: September 15, 2024
• Last Updated: February 24, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)