Clinical trial · Observational
Using Artificial Intelligence to Help Doctors Identify Different Skin Conditions and Improve Patient Care
A Multi-Reader Multi-Case Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (13)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-based medical device for aided diagnosis in Dermatology | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Healthcare Professionals (Primary Care Physicians and Dermatologists)
- description
- 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.
- interventionNames
- Device: AI-based medical device for aided diagnosis in Dermatology
Primary outcomes (1)
- measure
- Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support
- timeFrame
- Day 1
- description
- 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)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
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