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Health Care Science 2023, 2(4): 255-263 https://doi.org/10.1002/hcs2.61
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Large language models in health care: Development, applications, and challenges

Show Author's Information Rui Yang1,,  Ting Fang Tan2,,  Wei Lu3,  Arun James Thirunavukarasu4 ,  Daniel Shu Wei Ting2,5,,  Nan Liu5,6, ( )
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
StatNLP Research Group, Singapore University of Technology and Design, Singapore
University of Cambridge School of Clinical Medicine, Cambridge, UK
Duke‐NUS Medical School, Centre for Quantitative Medicine, Singapore, Singapore
Duke‐NUS Medical School, Programme in Health Services and Systems Research, Singapore, Singapore

Rui Yang and Ting Fang Tan are joint‐first authors.

Daniel Shu Wei Ting and Nan Liu are Joint‐senior authors.

Keywords:
AI, Large language model, Health care
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Yang R, Tan TF, Lu W, et al. Large language models in health care: Development, applications, and challenges. Health Care Science, 2023, 2(4): 255-263. https://doi.org/10.1002/hcs2.61
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Rui Yang1,, Ting Fang Tan2,, Wei Lu3, Arun James Thirunavukarasu4 , Daniel Shu Wei Ting2,5,, Nan Liu5,6, ( )
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
StatNLP Research Group, Singapore University of Technology and Design, Singapore
University of Cambridge School of Clinical Medicine, Cambridge, UK
Duke‐NUS Medical School, Centre for Quantitative Medicine, Singapore, Singapore
Duke‐NUS Medical School, Programme in Health Services and Systems Research, Singapore, Singapore

Rui Yang and Ting Fang Tan are joint‐first authors.

Daniel Shu Wei Ting and Nan Liu are Joint‐senior authors.

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.

Keywords: AI, Large language model, Health care

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Received: 10 April 2023
Accepted: 12 June 2023
Published: 24 July 2023
Issue date: August 2023

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© 2023 The Authors. Tsinghua University Press.

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