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

Authors :
Xiangbin Meng
Xiangyu Yan
Kuo Zhang
Da Liu
Xiaojuan Cui
Yaodong Yang
Muhan Zhang
Chunxia Cao
Jingjia Wang
Xuliang Wang
Jun Gao
Yuan-Geng-Shuo Wang
Jia-ming Ji
Zifeng Qiu
Muzi Li
Cheng Qian
Tianze Guo
Shuangquan Ma
Zeying Wang
Zexuan Guo
Youlan Lei
Chunli Shao
Wenyao Wang
Haojun Fan
Yi-Da Tang
Source :
iScience, Vol 27, Iss 5, Pp 109713- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Summary: 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.

Details

Language :
English
ISSN :
25890042
Volume :
27
Issue :
5
Database :
Directory of Open Access Journals
Journal :
iScience
Publication Type :
Academic Journal
Accession number :
edsdoj.492b56a667f4e4c821b972d5fe68655
Document Type :
article
Full Text :
https://doi.org/10.1016/j.isci.2024.109713