LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses Article Swipe
Sheng Wang
,
Fangyuan Zhao
,
Dan Bu
,
Yang Lu
,
Ming Gong
,
Hongjie Liu
,
Zhaohui Yang
,
Xiaoxi Zeng
,
Zhiyuan Yuan
,
Ben Wan
,
Jingbo Sun
,
Yang Wu
,
Lianhe Zhao
,
Xirun Wan
,
Wei Huang
,
Tao Wang
,
Mengtong Xu
,
Jianjun Luo
,
Jingjia Liu
,
Jianjun Zheng
,
Wei Zhang
,
Kang Zhang
,
Hongjia Zhang
,
Shu Wang
,
Runsheng Chen
,
Yi Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-64142-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-64142-2
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models can lighten the workload of clinicians and patients, yet their responses often include fabricated evidence, outdated knowledge, and insufficient medical specificity.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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-64142-2
- https://www.nature.com/articles/s41467-025-64142-2.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 35
- OpenAlex ID
- https://openalex.org/W4415113957
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415113957Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41467-025-64142-2Digital Object Identifier
- Title
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LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responsesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-10-13Full publication date if available
- Authors
-
Sheng Wang, Fangyuan Zhao, Dan Bu, Yang Lu, Ming Gong, Hongjie Liu, Zhaohui Yang, Xiaoxi Zeng, Zhiyuan Yuan, Ben Wan, Jingbo Sun, Yang Wu, Lianhe Zhao, Xirun Wan, Wei Huang, Tao Wang, Mengtong Xu, Jianjun Luo, Jingjia Liu, Jianjun Zheng, Wei Zhang, Kang Zhang, Hongjia Zhang, Shu Wang, Runsheng Chen, Yi ZhaoList of authors in order
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https://doi.org/10.1038/s41467-025-64142-2Publisher landing page
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https://www.nature.com/articles/s41467-025-64142-2.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41467-025-64142-2.pdfDirect OA link when available
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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-
35Number of works referenced by this work
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