%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e59069
%T Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
%A Zhang,Kuo
%A Meng,Xiangbin
%A Yan,Xiangyu
%A Ji,Jiaming
%A Liu,Jingqian
%A Xu,Hua
%A Zhang,Heng
%A Liu,Da
%A Wang,Jingjia
%A Wang,Xuliang
%A Gao,Jun
%A Wang,Yuan-geng-shuo
%A Shao,Chunli
%A Wang,Wenyao
%A Li,Jiarong
%A Zheng,Ming-Qi
%A Yang,Yaodong
%A Tang,Yi-Da
%+ Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, 49 North Garden Road, Beijing, 100191, China, 86 88396171, tangyida@bjmu.edu.cn
%K large language models
%K LLMs
%K digital health
%K medical diagnosis
%K treatment
%K multimodal data integration
%K technological fairness
%K artificial intelligence
%K AI
%K natural language processing
%K NLP
%D 2025
%7 7.1.2025
%9 Viewpoint
%J J Med Internet Res
%G English
%X Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.
%R 10.2196/59069
%U https://www.jmir.org/2025/1/e59069
%U https://doi.org/10.2196/59069