A Scoping Review of the Application of Large Language Models in Clinical Practice

Corresponding author: Zou Haiou, haiou5275@163.com

DOI: 10.12201/bmr.202406.00001

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  Abstract: Purpose/Significance This scoping review aims to examine the applications of Large Language Models (LLMs) in clinical practice comprehensively, providing references for their future implementation in clinical settings. Method/Process The research question was clarified initially, followed by a computerized search in databases such as PubMed, Embase, Wanfang, and CNKI, for literature related to the application of LLMs in clinical practice up until April 22, 2024. Literature was screened according to predefined inclusion and exclusion criteria, with selected studies undergoing content extraction, summarization, and analysis. Result/Conclusion A total of 4061 relevant articles were retrieved, and after screening, 32 articles were finally included to analyze the application directions, specific functions, advantages, limitations, etc. of LLM in clinical practice. It was found that LLM has practical value in providing treatment recommendations, assisting in disease diagnosis, health education, and analyzing text and imaging data. However, performance in terms of answer accuracy and individualization was not entirely satisfactory. Overall, LLM demonstrates significant potential in the field of clinical medical care and nursing, yet further optimization, fine-tuning, and research are needed to control application risks and confirm its scope of applicability.

  Key words: large language models; artificial intelligence; clinical practice; scoping review; ChatGPT

  Submit time: 7 June 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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