The introduction of Artificial Intelligence in Medical Education: A Narrative Review of Implementation Practice, Evaluation, and Methodological Barriers

Abstract

Artificial Intelligence (AI) is changing the face of medical education at an unprecedented rate, providing new opportunities for personalized learning, automated assessment, intelligent tutoring, clinical simulation, and data-driven decision support. Although this adoption is gaining momentum, the use of AI in medical curricula is still disjointed, and there is considerable diversity in the practices and approaches of implementation, assessment, and institutional preparedness. This is a narrative review of the existing data on the applications of AI tools in medical training, their educational utility, practical application, and effects on student learning. The review also presents methodological challenges, such as the inconsistent evaluation schemes, insufficient empirical validation, ethical issues, problems with the curriculum, readiness by faculty readiness, and unclear structural constraints, which are obstacles to scalable and standardized integration. This review offers a clear basis to support the evolution of AI-enhanced medical education by the educator, policymaker, and institutions by mapping the existing practices, defining gaps, and identifying recommendations to be implemented and researched in the future.

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