从证据到使用大型语言模型的建议:可行性研究
Weilong Zhao1,2, Danni Xia1,2, Ziying Ye1,2
1Department of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou, China.
Journal of evidence-based medicine
|September 12, 2025
概括
大型语言模型 (LLM) 通过快速合成信息,可以加速创建基于证据的实践指南. 这项研究显示了LLMs.
科学领域:
- 医疗保健中的人工智能
- 临床实践指南 临床实践指南
- 医疗信息学 医疗信息学
背景情况:
- 制定基于证据的实践指南是复杂的,需要大量的专业知识.
- 人工智能 (AI) 提供了简化指南制定的潜力.
- 本研究研究了使用五种大型语言模型 (LLM) 来生成指导方针建议的方法.
研究的目的:
- 评估使用LLM来从结构化证据中生成建议的可行性.
- 评估不同LLM产生的建议的一致性.
- 探索AI在加速准则制定方面的潜力.
主要方法:
- 开发了经过验证的一般和特定提示.
- 基于证据的健康和生活方式指南来源于PubMed.
- 从每个指南中随机提取一个建议及其支持证据.
- 五个LLM (ChatGPT-3.5,克劳德-3奏,巴德,ChatGLM-4,Kimi) 根据提供的证据提出了建议.
主要成果:
- 聊天GPT-3.5在证据综合和创新洞察力生成方面表现出色.
- 巴德始终与现有的指导原则保持一致.
- 克劳德专注于证据分析和减少不相关的信息.
- 聊天GLM-4平衡的证据提取和准则的遵守.
- 基米产生了简洁,有针对性的建议.
- 平均推一致性在50%至91.7%之间.
结论:
- 法律法规显示出加速制定基于证据的建议的巨大潜力.
- 法律法规可以有效地从结构化证据中提取和综合信息.
- 生成的建议与支持的证据保持一致.
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