The Application of Large Language Models in Medicine: A Scoping Review

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

iScience(2024)

Cited 0|Views22
No score
Abstract
This study systematically reviewed the application of Large Language Models (LLMs) in medicine, analysing 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.
More
Translated text
Key words
Large Language Models (LLMs),Medical Research,Geographical Distribution,Organ System Research,Medical Applications,IT Advancements,Medical Writing,Auxiliary Diagnosis,Patient Communication,Medical Education
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined