人工智能支持的健康发展指南问题
Bernardo Sousa-Pinto1, Rafael José Vieira1, Manuel Marques-Cruz1
1Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal (B.S.-P., R.J.V., M.M.-C., S.G.-M., L.P., M.M., A.M.P.).
Annals of internal medicine
|September 23, 2024
概括
大型语言模型 (LLM) 可以通过分析在线查询和直接生成来帮助生成指南问题,补充专家的意见. 这种方法有助于确定ARIA指南的新问题.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 传统上,指导性问题由专家小组制定.
- 确定全面和相关的问题对于高质量的临床指南至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 在生成临床指导问题的有用性.
- 探索两种不同的LLM驱动的问题开发方法:分析在线搜索查询和直接LLM生成.
- 提供实用的见解和从实施这些基于LLM的方法中吸取的经验教训.
主要方法:
- 通过谷歌趋势,评估LLM在识别与过敏性鼻炎相关的流行的在线搜索查询中的问题方面的表现.
- 负责LLM从患者和临床医生的角度直接生成指南问题.
- 手动结构化识别的查询和LLM生成的问题变成正式的指导问题.
主要成果:
- 在线查询的LLM分析确定了37个相关问题,其中22个是新问题,2个由小组优先考虑.
- 直接LLM生成产生了22个独特的相关问题,11个新问题,4个由小组优先考虑.
- 对于2024年ARIA指南,共有6个新问题,这些问题最初并未由专家组构想出来,这些问题被优先考虑.
结论:
- 基于LLM的方法有效地支持和增强了临床指导问题的传统发展.
- 这些方法可以确定新的和相关的问题,提高指导方针小组工作的全面性.
- 在制定指导问题的过程中,LLM提供了一种有价值的工具来补充专家驱动的流程.
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