Delving into the Practical Applications and Pitfalls of Large Language Models in Medical Education: Narrative Review - Li R; Wu T (Advances in Medical Education and Practice)
Rui Li,1 Tong Wu2– 4 1Emergency Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 4Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCorrespondence: Tong Wu, Department of Obstetrics and Gynecology, Tongji Hospital, No. 1095, Jiefang Avenue, Wuhan, 430030, People’s Republic of China, Email tongwu66@tjh.tjmu.edu.cnAbstract: Large language models (LLMs) have emerged as valuable tools in medical education, attracting substantial attention in recent years. They offer educators essential support in developing instructional plans, generating interactive materials, and facilitating efficient feedback mechanisms. Furthermore, LLMs enhance students’ language acquisition, writing proficiency, and creativity in educational activities. This review aims to examine the practical applications of LLMs in enhancing the educational and academic performance of both teachers and students, providing specific examples to demonstrate their effectiveness. Additionally, we address the inherent challenges associated with LLM implementation and propose viable solutions to optimize their use. Our study lays the groundwork for the broader integration of LLMs in medical education and research, ensuring the highest standards of medical learning and, ultimately, patient safety.Keywords: large language models, medical education, artificial intelligence, educator, automation bias, hallucination