From Questions to Clinical Recommendations: Large Language Models Driving Evidence-Based Clinical Decision Making

作     者：Li, Dubai Jiang, Nan Huang, Kangping Tu, Ruiqi Ouyang, Shuyu Yu, Huayu Qiao, Lin Yu, Chen Zhou, Tianshu Tong, Danyang Wang, Qian Li, Mengtao Zeng, Xiaofeng Tian, Yu Tian, Xinping Li, Jingsong 

作者机构：College of Biomedical Engineering and Instrument Science Zhejiang University Engineering Research Center of EMR and Intelligent Expert System Ministry of Education Hangzhou310027 China  Ministry of Science and Technology State Key Laboratory of Complex Severe and Rare Diseases Key Laboratory of Rheumatology and Clinical Immunology Ministry of Education Beijing100730 China Research Center for Scientific Data Hub Zhejiang Lab Hangzhou311121 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

主　　题：Diseases 

摘      要：Clinical evidence, derived from rigorous research and data analysis, provides healthcare professionals with reliable scientific foundations for informed decision-making. Integrating clinical evidence into real-time practice is challenging due to the enormous workload, complex professional processes, and time constraints. This highlights the need for tools that automate evidence synthesis to support more efficient and accurate decision making in clinical settings. This study introduces Quicker, an evidence-based clinical decision support system powered by large language models (LLMs), designed to automate evidence synthesis and generate clinical recommendations modeled after standard clinical guideline development processes. Quicker implements a fully automated chain that covers all phases, from questions to clinical recommendations, and further enables customized decision-making through integrated tools and interactive user interfaces. To evaluate Quicker’s capabilities, we developed the Q2CRBench-3 benchmark dataset, based on clinical guideline development records for three different diseases. Experimental results highlighted Quicker’s strong performance, with fine-grained question decomposition tailored to user preferences, retrieval sensitivities comparable to human experts, and literature screening performance approaching comprehensive inclusion of relevant studies. In addition, Quicker-assisted evidence assessment effectively supported human reviewers, while Quicker’s recommendations were more comprehensive and logically coherent than those of clinicians. In system-level testing, collaboration between a single reviewer and Quicker reduced the time required for recommendation development to 20-40 minutes. In general, our findings affirm the potential of Quicker to help physicians make quicker and more reliable evidence-based clinical decisions. © 2025, CC BY.