The application of large language models in medicine: A scoping review - Meng, Xiangbin; Yan, Xiangyu; Zhang, Kuo; Liu, Da; Cui, Xiaojuan; Yang, Yaodong; Zhang, Muhan; Cao, Chunxia; Wang, Jingjia; Wang, Xuliang; Gao, Jun; Wang, Yuan-Geng-Shuo; et al. (iScience, 2024)
This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare. [Display omitted] •LLMs transform healthcare in diagnostics, writing, and education•Multimodal LLMs show great future potential in healthcare•Global surge in LLM research for healthcare applications•Need for ethical LLM integration and empirical studies in clinics Artificial intelligence; Health informatics