在具有挑战性的住院患者管理场景中,大语言模型建议的变化
Susan Landon1, Thomas Savage1,2, S Ryan Greysen1,2,3
1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Journal of general internal medicine
|October 7, 2025
概括
大型语言模型 (LLM) 在复杂患者病例的临床建议中显示出显著的差异. 临床医生必须批判性地评估LLM输出,并保留最终决策权.
科学领域:
- 临床决策支持系统临床决策支持系统
- 医学中的人工智能
- 医疗信息学医学信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到临床工作流中.
- 他们在依赖判断的床边决策中的表现在很大程度上仍然没有特征.
- 了解LLM行为对于安全有效的临床实施至关重要.
研究的目的:
- 评估商业上可获得的LLMs的建议的变化.
- 评估常见住院患者管理场景的不同LLM内部和跨越LLM的一致性.
- 确定LLM在细微的临床决策中的潜在局限性.
主要方法:
- 进行了一项横截面模拟研究,使用了四个临床细节.
- 查询了六个LLM,包括通用和特定领域的模型.
- 每个LLM被提示五次,每一个片,以评估内部一致性和跨模型协议.
主要成果:
- 在所有测试的场景中,LLM的建议在所有测试场景中都有显著差异.
- 模式间的一致性很低,管理决策 (例如输血,抗凝血,出院) 的变化很大.
- 内部一致性有所不同,一些模型在重复查询中改变了建议.
结论:
- 广泛使用的LLM在复杂的住院患者管理决策的建议中表现出相当大的变化.
- 临床医生应将LLM输出视为补充信息,而不是最终指导.
- 需要进行进一步的研究,以开发解决模型不确定性的方法,并确保安全地将AI整合到临床实践中.
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