Medical Science: Updates and Prospects Vol. 9

Artificial intelligence (AI) has begun to significantly influence various fields, with medical education being a notable example. The integration of AI in medical education represents a technological evolution and a paradigm shift in how medical knowledge is acquired, applied, and retained. AI has moved from being a specialist topic in biomedical informatics to becoming a general educational concern across undergraduate, postgraduate, and continuing medical education. This transition has accelerated since the public diffusion of large language models, but it also rests on a longer trajectory involving machine learning, clinical decision support, adaptive learning, and simulation technologies. The central educational question is no longer whether AI will affect medical education, but how medical education should respond without compromising scientific rigour, ethical standards, or the humanistic foundations of practice. This narrative review examines how AI is reshaping the aims, content, methods, and governance of medical education. It synthesises literature on AI literacy, curriculum reform, personalised learning, assessment, faculty roles, virtual patients, generative AI, and professional accountability. The review argues that the future of medical education will depend less on teaching learners to use a single tool and more on developing durable capacities: critical data and AI literacy, interpretive judgement, ethical reasoning, communication, collaborative practice with digital systems, and the ability to recognise when human oversight must prevail. At the same time, the review highlights serious risks associated with opaque models, algorithmic bias, hallucinated outputs, privacy breaches, academic misconduct, over-reliance, and unequal access to technological infrastructure. Rather than treating AI as a separate technical elective, medical education will likely need a layered approach in which foundational AI concepts, domain-specific applications, and institutional governance are integrated longitudinally. The chapter concludes that AI can strengthen medical education when deployed as an augmentation of teaching, learning, assessment, and simulation, but only if implementation is guided by educational theory, transparent evaluation, robust governance, and an explicit commitment to patient safety and professional formation.