将SHAP和临床医生友好解释进行比较,发现了对临床决策行为的影响
Sujeong Hur1,2, Yura Lee3, Joongheum Park2,4,5
1Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
NPJ digital medicine
|September 26, 2025
概括
评估了使用可解释AI (XAI) 的临床决策支持系统 (CDSS). 提供临床解释,而不仅仅是结果或SHAP值,显著改善了临床医生的接受和信任.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 人与计算机的交互
背景情况:
- 临床决策显著影响患者的结果和生活质量.
- 由人工智能驱动的临床决策支持系统 (CDSS) 通常充当"黑子",阻碍信任和采用.
- 可解释AI (XAI) 旨在消除AI决策的神秘性,但其在临床环境中的有效性需要经验验证.
研究的目的:
- 为了比较临床医生在CDSS中不同XAI解释方法的接受度,信任度,满意度和可用性.
- 确定影响临床医生接受XAI驱动的CDSS的因素.
- 为在临床实践中实施XAI提供基于证据的建议.
主要方法:
- 一项平衡的研究设计,涉及63名外科医生和医生,他们以前曾处方血液制品.
- 参与者通过使用CDSS做出决定,并通过六个视频呈现三个解释方法之一 (仅结果,SHAP结果或临床解释).
- 调查问卷评估了接受度,信任度,满意度和可用性,进一步分析了与接受度相关的因素.
主要成果:
- 提供临床解释显著提高了临床医生的接受度,而不是"仅限于结果"或"SHapley添加式扩展 (SHAP) 结果".
- 发现临床医生的信任,满意度和可用性与接受CDSS有积极的相关性.
- 这项研究提供了经验证据,支持XAI-CDSS采用临床解释的优越性.
结论:
- 临床解释比"仅限于结果"或SHAP解释更有效,可以提高临床医生对AI驱动的CDSS的接受度.
- 信任,满意度和可用性是XAI-CDSS接受的关键因素.
- 战略XAI-CDSS实施的最佳实践应优先考虑明确,临床相关的解释,以促进对医疗保健的信任和采用.
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