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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; Ji, Jia-Ming; Qiu, Zifeng; Li, Muzi; Qian, Cheng; Guo, Tianze; Ma, Shuangquan; Wang, Zeying; Guo, Zexuan; Lei, Youlan; Shao, Chunli; Wang, Wenyao; Fan, Haojun; Tang, Yi-Da.
Afiliação
  • Meng X; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Yan X; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.
  • Zhang K; Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  • Liu D; Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China.
  • Cui X; Department of Cardiology, the First Hospital of Hebei Medical University, Graduate School of Hebei Medical University, Shi-jia-zhuang, Hebei, China.
  • Yang Y; School of Software & Microelectronics, Peking University, Beijing, China.
  • Zhang M; Institute for Artificial Intelligence, Peking University, Beijing, China.
  • Cao C; Institute for Artificial Intelligence, Peking University, Beijing, China.
  • Wang J; Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  • Wang X; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Gao J; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Wang YG; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Ji JM; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Qiu Z; Institute for Artificial Intelligence, Peking University, Beijing, China.
  • Li M; Peking University Health Science Center, Peking University First Hospital, Beijing, China.
  • Qian C; Peking University Health Science Center, Peking University People's Hospital, Beijing, China.
  • Guo T; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Ma S; Peking University Health Science Center, Beijing, China.
  • Wang Z; School of Biological Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China.
  • Guo Z; Department of Prosthodontics, Peking University School and Hospital of Stomatology, National Engineering Laboratory for Digital and Material Technology of Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, Beijing, China.
  • Lei Y; Peking University Health Science Center, Beijing, China.
  • Shao C; Peking University Health Science Center, Beijing, China.
  • Wang W; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Fan H; Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
  • Tang YD; Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
iScience ; 27(5): 109713, 2024 May 17.
Article em En | MEDLINE | ID: mdl-38746668
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: IScience Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: IScience Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China