Evidence map›Paper›PMID 41423701›Full record

ArticleNPJ digital medicine2025

Streamlining evidence based clinical recommendations with large language models.

Dubai Li, Nan Jiang, Kangping Huang, Ruiqi Tu, Shuyu Ouyang, Huayu Yu, Lin Qiao, Chen Yu, Tianshu Zhou, Danyang Tong and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. AI in respiratory care: findings from the GOLD report.Journal of translational medicine · 2026
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Dubai Li *College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.
Nan Jiang *Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Kangping HuangDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Ruiqi TuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.
Shuyu OuyangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.
Huayu YuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.
Lin QiaoDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Chen YuDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Tianshu ZhouResearch Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China.
Danyang TongResearch Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China.
Qian WangDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Mengtao LiDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Xiaofeng ZengDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.
Yu TianCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China. tyler@zju.edu.cn.
Xinping TianDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College; National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology; State Key Laboratory of Complex Severe and Rare Diseases; Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China. tianxp6@126.com.
Jingsong LiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China. ljs@zju.edu.cn.

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) No.2021-I2M-1-005Fundamental Research Funds for the Central Universities No.226-2025-00006National Key Research and Development Program of China No.2022YFC2504600National Key Research and Development Program of China No.2022YFC2504605National Natural Science Foundation of China 82572379Zhejiang Province Key Research and Development Plan No. 2024SSYS0010
6 · The paper itself

Abstract

Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.

Identifiers

PMID41423701
PMCPMC12749395

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.