LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses

doi:10.1038/s41467-025-64142-2

	LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses
	Wang, Sheng 1,2; Zhao, Fangyuan 1,2; Bu, Dechao 1,2; Lu, Yunwei 3; Gong, Ming 4; Liu, Hongjie 1,5; Yang, Zhaohui 1,2; Zeng, Xiaoxi 6,7; Yuan, Zhiyuan 8,9,10; Wan, Baoping 1; Sun, Jingbo 1,2; Wu, Yang 1; Zhao, Lianhe 1; Wan, Xirun 11; Huang, Wei 12; Wang, Tao 12; Xu, Mengtong 13; Luo, Jianjun 13; Liu, Jingjia 5; Zheng, Jianjun 5; Zhang, Wei 6,14; Zhang, Kang 15; Zhang, Hongjia 4; Wang, Shu 3; Chen, Runsheng 2,16; Zhao, Yi 1,2
	2025-10-13
发表期刊	NATURE COMMUNICATIONS
卷号	16 期号:1 页码:20
摘要	Large language models can lighten the workload of clinicians and patients, yet their responses often include fabricated evidence, outdated knowledge, and insufficient medical specificity. We introduce a general retrieval-augmented question-answering framework that continuously gathers up-to-date, high-quality medical knowledge and generates evidence-traceable responses. Here we show that this approach significantly improves the evidence validity, medical expertise, and timeliness of large language model outputs, thereby enhancing their overall quality and credibility. Evaluation against 15,530 objective questions, together with two physician-curated clinical test sets covering evidence-based medical practice and medical order explanation, confirms the improvements. In blinded trials, resident physicians indicate meaningful assistance in 87.00% of evidence-based medical scenarios, and lay users find it helpful in 90.09% of medical order explanations. These findings demonstrate a practical route to trustworthy, general-purpose language assistants for clinical applications.
DOI	10.1038/s41467-025-64142-2
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (National Science Foundation of China)[2022YFF1203303] ; National Key R&D Program of China[92474204] ; National Key R&D Program of China[32341019] ; National Key R&D Program of China[32070670] ; National Natural Science Foundation of China[2023030615] ; National Natural Science Foundation of China[2024020919] ; Ningbo Top Medical and Health Research Program ; Beijing Natural Science Foundation[2035] ; Beijing Natural Science Foundation[2023Z226] ; Beijing Natural Science Foundation[2024Z229] ; Ningbo Science and Technology Innovation Yongjiang ; Major Project of Guangzhou National Laboratory[KF2422-93] ; State Key Laboratory of Systems Medicine for Cancer
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001593286500034
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41619
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Hongjia; Wang, Shu; Chen, Runsheng; Zhao, Yi
作者单位	1.Chinese Acad Sci, Res Ctr Ubiquitous Comp Syst, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Peking Univ, Breast Ctr, Peoples Hosp, Beijing, Peoples R China 4.Capital Med Univ, Beijing Anzhen Hosp, Beijing, Peoples R China 5.Ningbo 2 Hosp, 41 Xibei Str, Ningbo, Peoples R China 6.Sichuan Univ, West China Hosp, West China Biomed Big Data Ctr, Chengdu, Sichuan, Peoples R China 7.Sichuan Univ, West China Hosp, Dept Nephrol, Chengdu, Sichuan, Peoples R China 8.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China 9.Fudan Univ, MOE Key Lab Computat Neurosci & Brain Inspired, Shanghai, Peoples R China 10.Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai, Peoples R China 11.Peking Union Med Coll Hosp, Ctr Gynecol Oncol, Beijing, Peoples R China 12.Henan Inst Adv Technol, Zhengzhou, Henan, Peoples R China 13.Chinese Acad Sci, Inst Biophys, Key Lab Epigenet Regulat & Intervent, Beijing, Peoples R China 14.Sichuan Univ, West China Hosp, Mental Hlth Ctr, Chengdu, Sichuan, Peoples R China 15.Macau Univ Sci & Technol, Fac Med, Macau, Peoples R China 16.Chinese Acad Sci, Inst Biophys, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Sheng,Zhao, Fangyuan,Bu, Dechao,et al. LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses[J]. NATURE COMMUNICATIONS,2025,16(1):20.
APA	Wang, Sheng.,Zhao, Fangyuan.,Bu, Dechao.,Lu, Yunwei.,Gong, Ming.,...&Zhao, Yi.(2025).LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses.NATURE COMMUNICATIONS,16(1),20.
MLA	Wang, Sheng,et al."LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses".NATURE COMMUNICATIONS 16.1(2025):20.