AIHealthcare Analytics

Overview

Generative AI is being adopted across medical and health professions education at an accelerating pace, with applications spanning clinical skills training, knowledge assessment, patient simulation, and curriculum support. The evidence base, however, remains nascent and methodologically heterogeneous.

Bibliometric Evidence (Updated)

A 2025 bibliometric analysis of GAI in clinical skills training specifically confirms that the field is in an early but rapidly expanding phase. Scholarly output is growing, with language-model-driven educational applications and associated ethical issues as the dominant research themes. Critically, bibliometric findings reflect research activity and knowledge structure rather than direct evidence of educational effectiveness — a distinction the authors emphasize explicitly.

Effectiveness and Safety Evidence

Direct empirical evidence demonstrating that GAI improves clinical skill acquisition remains limited. Most studies are descriptive or proof-of-concept in nature. Rigorous randomized or controlled designs capable of isolating GAI's contribution to learning outcomes are rare. Safety implications — including over-reliance on AI-generated content and potential propagation of clinical misinformation — remain understudied.

Gaps and Future Needs

The field urgently requires standardized evaluation frameworks and empirical study designs that can assess both effectiveness and safety. Ethical dimensions, including equity of access to AI educational tools and academic integrity, are increasingly recognized as research priorities alongside pedagogical outcomes.