Large language models for medicine: a survey

被引:0
|
作者
Zheng, Yanxin [1 ]
Gan, Wensheng [1 ]
Chen, Zefeng [1 ]
Qi, Zhenlian [2 ]
Liang, Qian [3 ]
Yu, Philip S. [4 ]
机构
[1] Jinan Univ, Coll Cyber Secur, Guangzhou 510632, Peoples R China
[2] Guangdong Ecoengn Polytech, Sch Informat Engn, Guangzhou 510520, Peoples R China
[3] Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen 518020, Peoples R China
[4] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA
基金
中国国家自然科学基金;
关键词
Artificial intelligence; Medical large language models; Healthcare applications; Ethical considerations; Potential directions; INFORMATION; CHATGPT;
D O I
10.1007/s13042-024-02318-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across various medical scenarios. In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced research directions and benefit researchers for future medical applications. We emphasize the advantages of medical LLMs in applications, as well as the challenges encountered during their development. Finally, we suggest directions for technical integration to mitigate challenges and potential research directions for the future of medical LLMs, aiming to meet the demands of the medical field better.
引用
收藏
页数:26
相关论文
共 50 条
  • [1] Large language models in medicine
    Arun James Thirunavukarasu
    Darren Shu Jeng Ting
    Kabilan Elangovan
    Laura Gutierrez
    Ting Fang Tan
    Daniel Shu Wei Ting
    [J]. Nature Medicine, 2023, 29 : 1930 - 1940
  • [2] Large language models in medicine
    Thirunavukarasu, Arun James
    Ting, Darren Shu Jeng
    Elangovan, Kabilan
    Gutierrez, Laura
    Tan, Ting Fang
    Ting, Daniel Shu Wei
    [J]. NATURE MEDICINE, 2023, 29 (08) : 1930 - 1940
  • [3] Large language models for science and medicine
    Telenti, Amalio
    Auli, Michael
    Hie, Brian L.
    Maher, Cyrus
    Saria, Suchi
    Ioannidis, John P. A.
    [J]. EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2024, 54 (06)
  • [4] Explainability for Large Language Models: A Survey
    Zhao, Haiyan
    Chen, Hanjie
    Yang, Fan
    Liu, Ninghao
    Deng, Huiqi
    Cai, Hengyi
    Wang, Shuaiqiang
    Yin, Dawei
    Du, Mengnan
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2024, 15 (02)
  • [5] Large Language Models in Finance: A Survey
    Li, Yinheng
    Wang, Shaofei
    Ding, Han
    Chen, Hang
    [J]. PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023, 2023, : 374 - 382
  • [6] Large language models in law: A survey
    Lai, Jinqi
    Gan, Wensheng
    Wu, Jiayang
    Qi, Zhenlian
    Yu, Philip S.
    [J]. AI Open, 2024, 5 : 181 - 196
  • [7] A survey on LoRA of large language models
    Mao, Yuren
    Ge, Yuhang
    Fan, Yijiang
    Xu, Wenyi
    Mi, Yu
    Hu, Zhonghao
    Gao, Yunjun
    [J]. Frontiers of Computer Science, 2025, 19 (07)
  • [8] A survey on large language models for recommendation
    Wu, Likang
    Zheng, Zhi
    Qiu, Zhaopeng
    Wang, Hao
    Gu, Hongchao
    Shen, Tingjia
    Qin, Chuan
    Zhu, Chen
    Zhu, Hengshu
    Liu, Qi
    Xiong, Hui
    Chen, Enhong
    [J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2024, 27 (05):
  • [9] A Survey on Evaluation of Large Language Models
    Chang, Yupeng
    Wang, Xu
    Wang, Jindong
    Wu, Yuan
    Yang, Linyi
    Zhu, Kaijie
    Chen, Hao
    Yi, Xiaoyuan
    Wang, Cunxiang
    Wang, Yidong
    Ye, Wei
    Zhang, Yue
    Chang, Yi
    Yu, Philip S.
    Yang, Qiang
    Xie, Xing
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2024, 15 (03)
  • [10] Creation and Adoption of Large Language Models in Medicine
    Shah, Nigam H.
    Entwistle, David
    Pfeffer, Michael A.
    [J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2023, 330 (09): : 866 - 869