Artificial Intelligence in Medical Education: Opportunities, Challenges, and Future Directions

Artificial intelligence (AI), particularly generative AI and large language models, is rapidly transforming medical education by introducing innovative approaches to personalize learning, simulate clinical scenarios, and generate educational content at scale. While AI offers promising opportunities to enhance learner engagement and skill development, it also raises important challenges related to data accuracy, algorithmic bias, transparency, ethical considerations, and academic integrity. This article presents a comprehensive review of AI applications within medical education, synthesizing current evidence on the benefits, risks, and future directions for integration. AI facilitates personalized learning pathways, virtual clinical simulations, and automated content creation, enabling scalable and flexible education. However, limitations such as the “black box” nature of AI, potential biases in outputs, privacy concerns, and risks of misuse highlight the need for cautious adoption. Developing AI literacy among learners and educators, reforming assessment strategies to focus on higher-order cognitive skills, and fostering interdisciplinary collaboration are critical to maximizing AI’s educational potential. Thoughtful and ethical integration of AI technologies into medical curricula can foster a learner-centered, adaptive, and equitable educational environment. By addressing the associated challenges proactively, medical education can better prepare future healthcare professionals for a complex, data-driven, and technology-rich clinical landscape.

Keywords:

Artificial intelligence , Medical education , Generative AI , AI literacy , Educational assessment