Evaluating an Artificial Intelligence Tool to Help Primary Care Doctors Diagnose Skin Conditions.

NCT07428941 | Completed

• 2 other identifiers: Legit.Health_PH_2024EUPAS1000000644
• Study Type: observational
• Target: 9
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Skin Lesions; Melanocytic Nevi; Melanoma (Skin Cancer); Urticaria; Pustular Psoriasis; Actinic Keratoses; Plaque Psoriasis; Hidradenitis Suppurativa (HS); Basal Cell Carcinoma of Skin

Keywords

Skin conditions; Rare dermatological diseases; Melanoma; Diagnositc accuracy; Referral optimization

Outcome Measures

Primary Outcomes (1)

• 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).

  Day 1

Secondary Outcomes (2)

• Change in Dermatology Referral Rate Assisted by Artificial Intelligence.

  Day 1

• Percentage of Cases Deemed Manageable via Remote Consultation.

  Day 1

Study Arms (1)

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

Interventions

AI-based medical device for aided diagnosis in dermatological conditions 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.

Primary Care Physicians

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. 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.

You may qualify if:

• Board-certified primary care physicians regardless of their professional experience.
• High-quality images of patients with different skin conditions.

You may not qualify if:

• Low-quality images of patients which can not be properly analyzed.

Sponsors & Collaborators

• AI Labs Group S.L: lead
• Puerta de Hierro University Hospital: collaborator

Study Sites (1)

AI Labs Group S.L.

Bilbao, Basque Country, 48001, Spain

Location

MeSH Terms

Conditions

Nevus, Pigmented; Melanoma; Carcinoma, Basal Cell; Urticaria; Keratosis, Actinic; Hidradenitis Suppurativa; Skin Diseases

Condition Hierarchy (Ancestors)

Nevus; Nevi and Melanomas; Neoplasms by Histologic Type; Neoplasms; Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Skin Neoplasms; Neoplasms by Site; Skin and Connective Tissue Diseases; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms, Basal Cell; Skin Diseases, Vascular; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases; Precancerous Conditions; Keratosis; Skin Diseases, Bacterial; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Skin Diseases, Infectious; Suppuration; Hidradenitis; Sweat Gland Diseases

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 4, 2024
• Primary Completion: September 13, 2024
• Study Completion: September 13, 2024
• Last Updated: February 24, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)