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问正确的问题:在开发临床咨询模板时使用大型语言模型进行基准测试

Liam G McCoy¹, David Wu², Sarita Khemani³

¹Division of Neurology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada2Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA, lmccoy@ualberta.ca.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

概括

大型语言模型 (LLM) 可以生成临床咨询模板,但难以简洁和优先级. 为了在医疗保健中有效地交换临床信息,需要进一步发展.

科学领域:

人工智能在医学中的应用
临床信息学临床信息学
自然语言处理自然语言处理.

背景情况:

电子咨询 (eConsults) 有助于医生有效沟通.
结构化模板对于清晰简洁的临床信息交换至关重要.
大型语言模型 (LLM) 显示了自动化模板生成的潜力.

研究的目的:

评估最先进的LLMs在生成结构化临床咨询模板方面的能力.
评估LLM生成的模板的临床连贯性,简洁性和优先级.
确定当前LLM在制造临床相关的eConsult方案方面的局限性.

主要方法:

利用了来自斯坦福大学 eConsult 团队的 145 个专家制作的 eConsult 模板.
评估的边境LLM包括o3,GPT-4o,Kimi K2,Claude 4 Sonnet,Llama 3 70B,以及双子座2.5 Pro.
采用了一个多代理管道,具有快速优化,语义自动升级和优先级分析.

主要成果:

模型实现了高全面性 (高达92.2%) 但产生过长的模板.
在长度限制下,LLM未能有效地优先考虑临床重要问题.
性能因医疗专业而异,精神病学和疼痛医学显著退化.

结论:

在改善结构化临床信息交换方面,LLM显得有前途.
目前的LLM需要加强评估方法,重点关注临床突出性和优先级.
解决长度限制和专业特定的细微差别对于eConsults中的实际LLM应用至关重要.

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