用户为中心的方法可解释AI开发用于医院临床决策支持:一个范围审查
Bethany A Van Dort1, Thomas Engelsma1,2, Stephanie Medlock3,2
1Amsterdam UMC location University of Amsterdam, eHealth Living & Learning Lab Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands.
Studies in health technology and informatics
|May 13, 2025
概括
本次审查强调了医疗保健专业人员在为临床决策支持系统 (CDSS) 设计可解释AI (XAI) 时需要更多的参与. 早期和多样化的用户参与对于在医院中创建有效和可用的AI工具至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 人与计算机的交互
背景情况:
- 可解释的人工智能 (XAI) 提高了基于AI的临床决策支持系统 (CDSS) 的透明度和可用性.
- 为了将XAI有效地整合到临床工作流程中,需要为医疗保健专业人员提供可理解,相关和可操作的解释.
- XAI-CDSS工具旨在改善医疗保健中的诊断,治疗计划和风险预测.
研究的目的:
- 调查最终用户参与XAI基础的CDSS设计和开发的程度和方法,用于医院环境.
- 在XAI-CDSS.开发过程中,确定参与医疗保健专业人员的常见做法和差距.
主要方法:
- 从Medline,Embase和Web of Science的文献进行系统的范围审查.
- 纳入标准侧重于详细说明最终用户参与XAI-CDSS设计的研究.
- 分析了11项已识别的研究,包括采访和焦点小组等定性方法.
主要成果:
- 采访和焦点小组,主要是与医生,是常见的参与方法.
- 只有四项研究涉及多个开发阶段的用户,并测试了各种解释技术.
- 质量评估揭示了一些研究中招聘策略和分析细节的局限性.
结论:
- 包括医生和护士在内的各种最终用户的早期和持续参与对于有效的XAI-CDSS开发至关重要.
- 与最终用户测试不同的解释技术是必要的,以确保认知对齐和可用性.
- 未来的研究应该优先考虑强大的用户参与策略和参与方法的详细报告.
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