在基于AI的临床决策支持系统中感知到信任和专业身份的威胁:基于AI过程设计特征的基于场景的实验研究
Sophia Ackerhans1, Kai Wehkamp2, Rainer Petzina2
1Kiel Institute of Responsible Innovation, University of Kiel, Kiel, Germany.
JMIR formative research
|March 26, 2025
概括
医疗保健中的可解释的人工智能建立了信任,并减少了医生身份威胁. 然而,问责功能可以增加这些威胁,显示AI.
科学领域:
- 医疗信息学 医疗信息学
- 人与计算机的交互
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 系统,包括临床决策支持系统 (CDSS),在医疗保健方面表现有前途,有时超过人类专家的性能.
- 然而,人工智能整合可能会挑战医疗专业人员的身份,侵蚀信任,可能导致拒绝这些技术.
研究的目的:
- 研究AI过程设计特征如何影响医生对AI解决方案的信任.
- 检查人工智能设计特征对专业身份所感知的威胁的影响.
- 研究的关键特征包括AI可解释性,工作流集成深度以及医生对AI驱动决策的问责制.
主要方法:
- 进行了一项基于网络的3因素的实验,与292名参与者 (医学学生和经验丰富的医生) 进行了实验.
- 参与者展示了涉及基于AI的CDSS用于败血症预测和预防的场景.
- 一个2x2x2的因数设计操纵了AI的解释性,集成深度和问责制.
主要成果:
- 人工智能解释性与信任和专业身份威胁感知有积极的相关性.
- 对人工智能的信任与对身份威胁的看法有负面关联,这表明部分调解效应.
- 人工智能建议深入整合到临床工作流程中,与信任有积极的相关性;问责制与身份威胁感知有积极的相关性.
结论:
- 人工智能过程设计显著塑造了专业的身份认知,信任作为调解者.
- 可解释的人工智能和深度工作流集成增强了信任,减轻了身份威胁.
- 可解释的人工智能和个人问责制可以直接增加感知到的威胁,突出了人工智能在医疗保健中的复杂行为影响.
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