Large Language Models in Medicine: Applications, Challenges, and Future Directions
Erlan Yu, Xuehong Chu, Wanwan Zhang, Xiangbin Meng, Yaodong Yang, Xunming Ji, Chuanjie Wu
Abstract
In recent years, large language models (LLMs) represented by GPT-4 have developed rapidly and performed well in various natural language processing tasks, showing great potential and transformative impact. The medical field, due to its vast data information as well as complex diagnostic and treatment processes, is undoubtedly one of the most promising areas for the application of LLMs. At present, LLMs has been gradually implemented in clinical practice, medical research, and medical education. However, in practical applications, medical LLMs still face numerous challenges, including the phenomenon of hallucination, interpretability, and ethical concerns. Therefore, in-depth exploration is still needed in areas of standardized evaluation frameworks, multimodal LLMs, and multidisciplinary collaboration in the future, so as to realize the widespread application of medical LLMs and promote the development and transformation in the field of global healthcare. This review offers a comprehensive overview of applications, challenges, and future directions of LLMs in medicine, providing new insights for the sustained development of medical LLMs.