Artificial intelligence tools in dermatology education: A scoping review on their application, efficacy, and limitations.

Abstract

Introduction Artificial Intelligence (AI) is increasingly explored for medical education, and dermatology, with its visual diagnostic focus, holds promise for AIenhanced learning. However, evidence on its educational effectiveness remains limited and fragmented. This scoping review aimed to assess the evidence base for applications and limitations of AI-based educational tools in dermatology. Method A systematic search of PubMed, Embase, Web of Science, Scopus, and PsycINFO up to July 2025 was conducted. Data were synthesised narratively, considering types of AI interventions and their assessed outcomes. These were analysed with the Cost, Usability, Credibility, Fairness, Accountability, Transparency, Explainability (CUC-FATE) framework. Study quality was assessed with ROBINS-I and a COSMIN-informed checklist. Results A total of 827 records were screened, with 360 duplicates removed, yielding 467 studies. Six full-length studies and 1 conference abstract met inclusion criteria, mostly from 2023 to 2025. These explored AI-generated clinical images, Large Language Model-generated vignettes, intelligent tutoring systems, and clinical decision support tools. Content validation studies generally reported favourable ratings for accuracy, clarity, and educational utility, while intervention studies suggested possible benefits for learning performance and diagnostic accuracy. Usability and credibility were commonly assessed, whereas cost, accountability, fairness, transparency, and explainability were rarely examined. Conclusion Most of the studies reviewed had high risk of bias, small sample sizes, and limited methodological rigour, with significant heterogeneity in examined educational outcomes limiting synthesis. While current initial studies on AI hold promise, this scoping review underscores the need for more robust studies with standardised evaluation frameworks, prioritising ethical principles such as fairness, explainability, and accountability for safe integration in training.