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大语言模型在医学中的应用:一项范围综述。

The application of large language models in medicine: A scoping review.

作者信息

Meng Xiangbin, Yan Xiangyu, Zhang Kuo, Liu Da, Cui Xiaojuan, Yang Yaodong, Zhang Muhan, Cao Chunxia, Wang Jingjia, Wang Xuliang, Gao Jun, Wang Yuan-Geng-Shuo, Ji Jia-Ming, Qiu Zifeng, Li Muzi, Qian Cheng, Guo Tianze, Ma Shuangquan, Wang Zeying, Guo Zexuan, Lei Youlan, Shao Chunli, Wang Wenyao, Fan Haojun, Tang Yi-Da

机构信息

Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.

State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.

出版信息

iScience. 2024 Apr 23;27(5):109713. doi: 10.1016/j.isci.2024.109713. eCollection 2024 May 17.


DOI:10.1016/j.isci.2024.109713
PMID:38746668
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11091685/
Abstract

This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.

摘要

本研究系统回顾了大语言模型(LLMs)在医学中的应用,通过广泛的文献检索分析了550项选定的研究。像ChatGPT这样的大语言模型通过增强诊断、医学写作、教育和项目管理改变了医疗保健。它们协助起草医疗文件、创建培训模拟并简化研究流程。尽管它们在辅助诊断和改善医患沟通方面的效用不断增加,但挑战依然存在,包括上下文理解的局限性和过度依赖的风险。与大语言模型相关的研究激增表明,研究重点集中在医学写作、诊断和患者沟通上,但也强调了在考虑验证、伦理问题以及与传统医疗实践的平衡的情况下,需要谨慎整合。未来的研究方向建议聚焦于多模态大语言模型、更深入的算法理解,并确保在医疗保健中负责任、有效地使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/445f95a929a5/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/a6b87686cf66/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/a0f3a147b739/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/f49444aaf05a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/758622355fbc/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/06603a6a850a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/445f95a929a5/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/a6b87686cf66/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/a0f3a147b739/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/f49444aaf05a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/758622355fbc/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/06603a6a850a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c17a/11091685/445f95a929a5/gr5.jpg

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[2]
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[3]
Large Language Models for Adverse Drug Events: A Clinical Perspective.

J Clin Med. 2025-8-4

[4]
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JMIR Med Inform. 2025-8-13

[5]
A multi-dimensional performance evaluation of large language models in dental implantology: comparison of ChatGPT, DeepSeek, Grok, Gemini and Qwen across diverse clinical scenarios.

BMC Oral Health. 2025-7-28

[6]
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Open Forum Infect Dis. 2025-6-23

[7]
MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning.

medRxiv. 2025-7-11

[8]
Automated MRI protocoling in neuroradiology in the era of large language models.

Radiol Med. 2025-7-11

[9]
Large language models for disease diagnosis: a scoping review.

NPJ Artif Intell. 2025

[10]
AI-Powered Problem- and Case-based Learning in Medical and Dental Education: A Systematic Review and Meta-analysis.

Int Dent J. 2025-8

本文引用的文献

[1]
Large Language Models and Empathy: Systematic Review.

J Med Internet Res. 2024-12-11

[2]
Structured information extraction from scientific text with large language models.

Nat Commun. 2024-2-15

[3]
Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.

iScience. 2024-1-24

[4]
LARGE LANGUAGE MODELS (LLMS) AND CHATGPT FOR BIOMEDICINE.

Pac Symp Biocomput. 2024

[5]
Leveraging Large Language Models for Decision Support in Personalized Oncology.

JAMA Netw Open. 2023-11-1

[6]
A study of generative large language model for medical research and healthcare.

NPJ Digit Med. 2023-11-16

[7]
ChatGPT has entered the classroom: how LLMs could transform education.

Nature. 2023-11

[8]
Large language models propagate race-based medicine.

NPJ Digit Med. 2023-10-20

[9]
Harnessing large language models (LLMs) for candidate gene prioritization and selection.

J Transl Med. 2023-10-16

[10]
Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures.

JAMA Netw Open. 2023-10-2

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