Streamlining evidence based clinical recommendations with large language models
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914241821278208 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Streamlining evidence based clinical recommendations with large language models 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 Computation and Language Artificial Intelligence 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. |
| title | Streamlining evidence based clinical recommendations with large language models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10282 |