Artificial Intelligence in Medical Education: Opportunities and Challenges

Abstract

Background: Artificial intelligence (AI), especially large language models (LLMs), is quickly gaining access to medical education, introducing new possibilities to enhance, customize, and expand teaching and learning processes. Their ability to generate natural language outputs supports a wide range of uses in education, evaluation, and faculty development. Objective: This short communication aims to highlight the current uses, benefits, risks, and future directions for the integration of LLM into medical education. Perspective: Current applications of AI in medical education include personalized tutoring, clinical reasoning simulations, automated assessment item generation, and curriculum development. While its benefits include scalability, flexible pedagogy, and reduction of faculty workload. Limitations include factual errors, bias propagation, lack of transparency, and potential disintegration of independent reasoning. Further, ethical considerations of AI encompass academic integrity, patient safety, data security, and equity of access. Research priorities involve evaluating learning outcomes, safety of simulated practice, and best practices for human–AI collaboration. Conclusion: LLMs hold transformative potential for medical education if integrated with faculty oversight, AI literacy training, and robust validation processes. Responsible adoption should prioritize accuracy, transparency, and learner competence, ensuring technology serves as a complement, not a substitute for human expertise. Bangladesh Journal of Infectious Diseases, June 2025;12(1):189-194

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