[1]XIAO Jianli,XU Dongzhou,WANG Hao,et al.Survey of large language models in healthcare[J].CAAI Transactions on Intelligent Systems,2025,20(3):530-547.[doi:10.11992/tis.202405003]
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Survey of large language models in healthcare

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 530-547 Column: 综述 Public date: 2025-05-05
Title:
Survey of large language models in healthcare
Author(s):
XIAO Jianli1;  XU Dongzhou1;  WANG Hao2;  LIU Min3;  ZHOU Lei4;  ZHU Lin4;  GU Song5
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Department of Cardiothoracic Surgery, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China;
3. Department of Gynecology of Integrated Traditional Chinese and Western Medicine, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China;
4. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
5. Trauma Center, Shanghai General Hospital, Shanghai 201620, China
Keywords:
artificial intelligence; deep learning; Transformer; large language model; intelligent healthcare; data analysis; image processing; computer vision
CLC:
TP18
DOI:
10.11992/tis.202405003
Abstract:
Deep learning (DL) is a popular research area in artificial intelligence. It simulates the data processing mechanism of the human brain by constructing multilayer artificial neural networks. Large language models (LLMs) based on the DL architecture can understand and generate human language by analyzing enormous data without programming instructions. Thus, LLMs are widely employed in various domains, such as natural language processing, computer vision, intelligent healthcare, and intelligent transportation. This article summarizes the application of LLMs in the healthcare sector, exploring their basic training processes, specific strategies for executing healthcare tasks, and their applications in specific healthcare scenarios. It also discusses the challenges of applying LLMs to the healthcare field, including the lack of transparency in decision-making processes, the accuracy of the output contents, and issues related to privacy and ethics. Thereafter, several strategies for addressing these issues are discussed. Finally, the future development trends of LLM in healthcare, as well as its criticality in promoting human health, are discussed.
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