TY - JOUR
T1 - Application of large language models in medicine
AU - Liu, Fenglin
AU - Zhou, Hongjian
AU - Gu, Boyang
AU - Zou, Xinyu
AU - Huang, Jinfa
AU - Wu, Jinge
AU - Li, Yiru
AU - Chen, Sam S.
AU - Hua, Yining
AU - Zhou, Peilin
AU - Liu, Junling
AU - Mao, Chengfeng
AU - You, Chenyu
AU - Wu, Xian
AU - Zheng, Yefeng
AU - Clifton, Lei
AU - Li, Zheng
AU - Luo, Jiebo
AU - Clifton, David A.
N1 - Publisher Copyright:
© Springer Nature Limited 2025.
PY - 2025/6
Y1 - 2025/6
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105002171981
U2 - 10.1038/s44222-025-00279-5
DO - 10.1038/s44222-025-00279-5
M3 - Review article
AN - SCOPUS:105002171981
SN - 2731-6092
VL - 3
SP - 445
EP - 464
JO - Nature Reviews Bioengineering
JF - Nature Reviews Bioengineering
IS - 6
ER -