CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis

The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are: * What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images? * How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians? Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians. Participants (physicians acting as clinical readers) will: * Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive. * Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results. * Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.

NCT Number	NCT07415291
Lead Sponsor	Istanbul Training and Research Hospital
Conditions	Solitary Skin Lesions, Skin Neoplasms, Skin Neoplasm Malignant
Enrollment	17,625 participants
Start Date	2026-01-15
Primary Completion	2026-03-30 (estimated)
Study Completion	2026-05-31 (estimated)
Updated on ClinicalTrials.gov	2026-02-19