Harnessing the potential of large language models in medical education: promise and pitfalls
Trista M. Benítez, Yueyuan Xu, J. Donald Boudreau, Alfred Wei Chieh Kow, Fernando Bello, Le Van Phuoc, Xiaofei Wang, Xiaodong Sun, Gkk Leung, Yanyan Lan, Ya Xing Wang, Davy Cheng, Yih Chung Tham, Tien Yin Wong, Kevin C. Chung
Abstract
OBJECTIVES: To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum. PROCESS: Narrative review of published literature contextualized by current reports of LLM application in medical education. CONCLUSIONS: LLMs like OpenAI's ChatGPT can potentially revolutionize traditional teaching methodologies. LLMs offer several potential advantages to students, including direct access to vast information, facilitation of personalized learning experiences, and enhancement of clinical skills development. For faculty and instructors, LLMs can facilitate innovative approaches to teaching complex medical concepts and fostering student engagement. Notable challenges of LLMs integration include the risk of fostering academic misconduct, inadvertent overreliance on AI, potential dilution of critical thinking skills, concerns regarding the accuracy and reliability of LLM-generated content, and the possible implications on teaching staff.