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Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine

Authors :
Kuo Zhang
Xiangbin Meng
Xiangyu Yan
Jiaming Ji
Jingqian Liu
Hua Xu
Heng Zhang
Da Liu
Jingjia Wang
Xuliang Wang
Jun Gao
Yuan-geng-shuo Wang
Chunli Shao
Wenyao Wang
Jiarong Li
Ming-Qi Zheng
Yaodong Yang
Yi-Da Tang
Source :
Journal of Medical Internet Research, Vol 27, p e59069 (2025)
Publication Year :
2025
Publisher :
JMIR Publications, 2025.

Abstract

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Details

Language :
English
ISSN :
14388871
Volume :
27
Database :
Directory of Open Access Journals
Journal :
Journal of Medical Internet Research
Publication Type :
Academic Journal
Accession number :
edsdoj.89e3e1e8cf84e459df4ebc0f820f8f0
Document Type :
article
Full Text :
https://doi.org/10.2196/59069