在临床决策中以人为中心的可解释性评估:对文献的批判性审查
Jenny M Bauer1, Martin Michalowski1
1School of Nursing, University of Minnesota, Minneapolis, MN 55455, United States.
概括
医疗保健提供者需要以人为中心的评估,以在临床决策中解释AI. 目前的方法专注于模型,但未来的研究应该开发全面的,以人为中心的评估工具,以便在实践中使用.
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:
- 可解释的人工智能 (XAI) 方法越来越多地用于支持临床决策 (CDM).
- 医疗保健提供者 (HCP) 对XAI的评估对于有效的护理点实施至关重要.
- 当前的评估方法往往缺乏以人为中心的视角,专注于以模型为中心的指标.
研究的目的:
- 综合审查和总结医疗保健专家对患者特异性CDM的XAI解释的评估.
- 突出需要以人为中心的评估方法,以符合HCP需求和目标的方法.
- 为了确定临床可解释性的潜在共同措施.
主要方法:
- 在Ovid Medline和Scopus数据库中进行的系统文献审查.
- 遵守医学研究所的方法标准和PRISMA指南.
- 使用MaxQDA软件与特定设计评估工具进行数据提取和分析.
主要成果:
- 从2673个独特的记录中,包括了25项研究,重点是医生和诊断成像.
- 排除标准涉及HCP评估,医疗保健使用,可解释AI,初级研究和语言的定义.
- 确定了广泛的评估措施,表明了临床可解释性的潜在共同指标:可解释性,可信度和临床价值.
结论:
- 可能存在一种常见的临床可解释性衡量标准,通过可解释性,忠实性和临床价值来表示.
- 有一个重要的机会,从以模型为中心的转向以人为中心的评估指标.
- 未来的研究应该开发一个全面的,以人为中心的评估模型和一个有效的工具,以支持医疗人员在临床实践中采用XAI.
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