Streamlining evidence based clinical recommendations with large language models

作者：Dubai Li, Nan Jiang, Kangping Huang, Ruiqi Tu, Shuyu Ouyang, Hui Yu, Qiao Lin, Yu Chen, Tianshu Zhou, Danyang Tong, Qian Wang, Mengtao Li, Xiaofeng Zeng, Yu Tian, Xinping Tian, Jingsong Li · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-02273-y · 被引用次数：10 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging、Machine Learning in Healthcare

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.