JMIR Publications

ABSTRACT

The integration of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Examples of promising applications of LLMs include curriculum development, augmenting teaching methodologies, crafting personalized study plans and learning materials, designing comprehensive assessment plans, improving the evaluation process, interpreting unstructured medical data, facilitating medical research, and implementing programmatic enhancements for medical education programs. However, the use of LLMs in medical education raises several challenges related to algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns. As the educational paradigm shifts from information-driven to AI-driven practices, it is crucial to explore the full potential of generative LLMs technologies while addressing the concerns and challenges that arise in medical education to better understand how to utilize such tools effectively and appropriately. The objective of this paper is to explore the opportunities and challenges of using LLMs in medical education. The insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.