用大模型自动生成临床指南，提速6倍以上。
Streamlining evidence based clinical recommendations with large language models
- 基于大模型构建端到端证据整合流程，模拟专业指南制定。
- 在3种疾病上实现近全覆盖的文献筛选与精准问题拆解。
- 医生协作下推荐生成时间压缩至20-40分钟，更完整准确。
临床证据是医疗决策的基础，但将其融入实时诊疗仍面临工作量大、流程复杂和时间紧张等挑战。本文提出Quicker，一个基于大语言模型的系统，可自动化完成证据合成并生成符合标准指南开发流程的临床推荐。该系统从临床问题出发，提供端到端的推荐生成支持，并通过集成工具与交互界面实现个性化决策。为评估其复现指南制定过程的能力，研究构建了基于三种疾病指南记录的Q2CRBench-3基准。实验表明，Quicker在问题分解、专家对齐的文献检索及近全覆盖的文献筛选方面表现优异；其辅助下提取的研究数据准确性更高，生成的推荐也比医生手写版本更全面、连贯。系统级测试显示，仅需一名参与者配合，推荐开发时间缩短至20-40分钟。总体结果表明，Quicker具备显著提升循证临床决策效率与可靠性的潜力。
原文摘要 · Abstract (English)
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.
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