大型语言模型生成的尾炎临床实践指南
Amy Boyle1, Bright Huo2, Patricia Sylla3
1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Surgical endoscopy
|April 18, 2025
概括
大型语言模型 (LLM) 可以通过生成搜索语法和执行数据分析来帮助临床指南的开发. 然而,他们目前不能独立进行文献搜索或进行查和偏见风险评估.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床实践指南开发 临床实践指南开发
背景情况:
- 临床实践指南对于基于证据的患者护理至关重要,但开发需要大量资源.
- 大型语言模型 (LLM) 在学术写作和系统评论中表现有希望,但它们在指导方针开发中的作用尚未被探索.
- 本研究评估了指导方针开发的所有阶段的LLM能力,使用外科尾炎指导方针作为基准.
研究的目的:
- 评估当前大型语言模型 (LLM) 的能力,以支持临床实践准则开发的各个阶段.
- 使用AGREE-S工具,将LLM生成的指南与已建立的指南的质量进行比较.
主要方法:
- 基于SAGES尾炎指南的关键问题和PICOs的工程提示以指导LLM任务.
- 在2024年2月21日查询聊天GPT-4,谷歌双子座,共识和困惑.
- 通过使用"外科研究和评估指南评估" (AGREE-S) 仪器对LLM成果进行定性评估和定量评估.
主要成果:
- 法律学成功生成了搜索语法,进行了数据分析,并应用了GRADE和证据到决策框架进行建议.
- 法律学士无法独立进行系统的文献搜索,选,数据提取或偏见风险评估.
- 在AGREE-S工具上,LLM衍生指南得分为119/156,24个领域中的19个在SAGES指南的两个点内得分.
结论:
- 法律法规显示,有潜力简化指导方针的制定,减少特定任务的时间和资源负担.
- 在指导方针开发中,LLMs的能力随着新模型的进步而不断发展.
- 多学科合作和进一步的研究对于安全有效地将LLMs纳入指南创建是必不可少的.
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