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<p>Large language models (LLMs), such as ChatGPT, have received great attention owing to their capabilities for understanding and generating human language. Despite a trend in researching the application of LLMs in supporting different medical tasks (such as enhancing clinical diagnostics and providing medical education), a comprehensive assessment of their development, practical applications and outcomes in the medical space is still missing. Therefore, this Review aims to provide an overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we discuss the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. In terms of deployment, we compare different LLMs across various medical tasks and with state-of-the-art lightweight models.</p>

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Application of large language models in medicine

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Fenglin Liu,
  • Hongjian Zhou,
  • Boyang Gu,
  • Xinyu Zou,
  • Jinfa Huang,
  • Jinge Wu,
  • Yiru Li,
  • Sam S. Chen,
  • Yining Hua,
  • Peilin Zhou,
  • Junling Liu,
  • Chengfeng Mao,
  • Chenyu You,
  • Xian Wu,
  • Yefeng Zheng,
  • Lei Clifton,
  • Zheng Li,
  • Jiebo Luo,
  • David A. Clifton

摘要

Large language models (LLMs), such as ChatGPT, have received great attention owing to their capabilities for understanding and generating human language. Despite a trend in researching the application of LLMs in supporting different medical tasks (such as enhancing clinical diagnostics and providing medical education), a comprehensive assessment of their development, practical applications and outcomes in the medical space is still missing. Therefore, this Review aims to provide an overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we discuss the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. In terms of deployment, we compare different LLMs across various medical tasks and with state-of-the-art lightweight models.