Artificial intelligence (AI) is rapidly transforming medical education by providing new approaches for knowledge acquisition, clinical training, and decision-making support. This narrative review synthesizes recent literature on AI applications in undergraduate, postgraduate, and continuing medical education. Key domains include adaptive learning platforms, natural language processing for educational resources, virtual simulation, automated feedback, and assessment technologies. Evidence suggests that AI improves personalization, efficiency, and objectivity in training, while also enabling innovative pedagogical models such as intelligent tutoring systems and competency-based progression. However, challenges remain in terms of data quality, faculty readiness, ethical considerations, and integration into existing curricula. This review highlights both the opportunities and limitations of AI in reshaping medical education, emphasizing the need for rigorous validation, interdisciplinary collaboration, and regulatory guidance. Future directions include hybrid AI–human teaching models, transparent algorithms, and equitable access to AI-driven education globally.
Suren Kanayan (2025) studied this question.