用于共享决策的比较表的自动生成:比较人为生成的表 (选项网格),搜索引擎过程和来自四个大型语言模型的输出
1Pharmaceutical Society of Ireland, Fenian Street, Dublin 2, Ireland.
Patient education and counseling
|September 23, 2025
概括
人工智能 (AI) 可以生成用于共享决策的比较表,但它们的准确性和可读性略低于人类创建的表. 人工智能生成的表格需要在患者使用之前仔细检查事实.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者决策辅助工具 患者决策辅助工具
背景情况:
- 共享决策对于以患者为中心的护理至关重要.
- 比较表,如选项网格TM,帮助患者了解治疗选择.
- 人工智能 (AI) 产生这些助手的潜力在很大程度上尚未被探索.
研究的目的:
- 评估大型语言模型 (LLM) 在生成用于共享决策的比较表方面的能力.
- 为了比较人工智能生成的表与人类专家创建的表 (Option GridTM) 的准确性和内容.
主要方法:
- 使用LLM (OpenBioLLM-70b,ChatGPT模型,OpenBioLLM-8b) 创建了四个比较表.
- 还进行了谷歌搜索模拟,以创建类似患者的比较表.
- 对表格的准确性和内容覆盖率进行了评估,并与人类生成的Option GridTM对膝关节置换场景进行了评估.
主要成果:
- 在LLMs和谷歌搜索中,表格的信息项数量不同,谷歌的信息项数量最多,OpenBioLLM-8b的信息项数量最少.
- 与人类的选项网格相比,ChatGPT和OpenBioLLM-70b的准确率达到97%,而OpenBioLLM-8b和谷歌搜索的准确率达到95%.
- 人类生成的表格显示出卓越的可读性,AI模型遗漏了关于替代干预的信息.
结论:
- 通过LLM生成的比较表的准确性略低 (3-5%) 和潜在的可读性可能低于人类创建的.
- 人工智能生成的表格需要在临床使用前进行彻底的事实检查和编辑.
- LLM显示出扩大基于证据的比较表生产的潜力,帮助患者做出决策.
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