Clinical trial · Observational
Evaluating an Artificial Intelligence Tool to Help Primary Care Doctors Diagnose Skin Conditions.
A Multi-Reader Multi-Case (MRMC) Study Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for 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 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
Conditions (9)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Actinic Keratoses | — | UNRESOLVED | — |
| Basal Cell Carcinoma of Skin | Skin Basal Cell Carcinoma | ALIAS | 0.90 |
| Hidradenitis Suppurativa (HS) | Hidroacanthoma Simplex | ALIAS | 0.85 |
| Melanocytic Nevi | Pigmented Nevus | ALIAS | 0.90 |
| Melanoma (Skin Cancer) | Melanoma | ONTOLOGY_EXACT | 0.85 |
| Plaque Psoriasis | — | UNRESOLVED | — |
| Pustular Psoriasis | — | UNRESOLVED | — |
| Skin Lesions | — | UNRESOLVED | — |
| Urticaria |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-based medical device for aided diagnosis in dermatological conditions | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Primary Care Physicians
- 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. * 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.
- interventionNames
- Device: AI-based medical device for aided diagnosis in dermatological conditions
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 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).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Board-certified primary care physicians 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)
Data not yet available