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

NCT07428954 | Completed

• 2 other identifiers: Legit.Health_SAN_2024EUPAS1000000911
• Study Type: observational
• Target: 16
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Dermatitis; Melanoma (Skin Cancer); Alopecia; Urticaria; Granuloma Annulare; Herpes; Tinea; Psoriasis; Onychomychosis; Pressure Ulcer (PU); Nevus; Seborrheic Keratoses; Acne

Keywords

Skin conditions; Dermatologists; Primary Care Physicians; Diagnostic accuracy

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 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 Outcomes (3)

• Change in Dermatology Referral Rate Assisted by Artificial Intelligence.

  Day 1

• Percentage of Cases Deemed Manageable via Remote Consultation.

  Day 1

• Clinical Utility and Usability Scores for Diagnostic 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 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

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 primary care practitioners and dermatologists, 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

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

Dermatitis; Melanoma; Alopecia; Urticaria; Granuloma Annulare; Keratosis, Seborrheic; Herpes Simplex; Tinea; Psoriasis; Acne Vulgaris; Pressure Ulcer; Nevus; Skin Diseases

Condition Hierarchy (Ancestors)

Skin and Connective Tissue Diseases; Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Nerve Tissue; Nevi and Melanomas; Skin Neoplasms; Neoplasms by Site; Hypotrichosis; Hair Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Skin Diseases, Vascular; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases; Necrobiotic Disorders; Collagen Diseases; Connective Tissue Diseases; Granuloma; Pathologic Processes; Keratosis; Herpesviridae Infections; DNA Virus Infections; Virus Diseases; Infections; Skin Diseases, Viral; Skin Diseases, Infectious; Dermatomycoses; Mycoses; Bacterial Infections and Mycoses; Skin Diseases, Papulosquamous; Acneiform Eruptions; Sebaceous Gland Diseases; Skin Ulcer

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: October 10, 2024
• Study Completion: October 10, 2024
• Last Updated: February 24, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)