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Large language models in health care: Development, applications, and challenges.

作者信息

Yang Rui, Tan Ting Fang, Lu Wei, Thirunavukarasu Arun James, Ting Daniel Shu Wei, Liu Nan

机构信息

Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.

Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore.

出版信息

Health Care Sci. 2023 Jul 24;2(4):255-263. doi: 10.1002/hcs2.61. eCollection 2023 Aug.


DOI:10.1002/hcs2.61
PMID:38939520
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11080827/
Abstract

Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training. In the last 3 years, these domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df22/11080827/35a1993f8fd2/HCS2-2-255-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df22/11080827/35a1993f8fd2/HCS2-2-255-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df22/11080827/35a1993f8fd2/HCS2-2-255-g002.jpg

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A comprehensive qualitative analysis of patient dialogue summarization using large language models applied to noisy, informal, non-English real-world data.

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[2]
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[3]
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[4]
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[5]
Evaluation of large language models as a diagnostic tool for medical learners and clinicians using advanced prompting techniques.

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[6]
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J Med Internet Res. 2025-7-30

[7]
The Use of ChatGPT-4.0 to Simplify Breast Pathology Reports: A Study on Readability and Accuracy.

Ann Surg Oncol. 2025-7-21

[8]
Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.

J Med Internet Res. 2025-7-11

[9]
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[10]
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本文引用的文献

[1]
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.

Cureus. 2023-6-24

[2]
Large language models encode clinical knowledge.

Nature. 2023-8

[3]
ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and Operative Notes.

Semin Ophthalmol. 2023-7

[4]
Is the Algorithm Good in a Bad World, or Has It Learned to be Bad? The Ethical Challenges of "Locked" Versus "Continuously Learning" and "Autonomous" Versus "Assistive" AI Tools in Healthcare.

Am J Bioeth. 2023-5

[5]
Trialling a Large Language Model (ChatGPT) in General Practice With the Applied Knowledge Test: Observational Study Demonstrating Opportunities and Limitations in Primary Care.

JMIR Med Educ. 2023-4-21

[6]
Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios.

J Med Syst. 2023-3-4

[7]
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models.

PLOS Digit Health. 2023-2-9

[8]
Generating scholarly content with ChatGPT: ethical challenges for medical publishing.

Lancet Digit Health. 2023-3

[9]
ChatGPT: the future of discharge summaries?

Lancet Digit Health. 2023-3

[10]
How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment.

JMIR Med Educ. 2023-2-8

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