加强临床决策支持:在医疗保健仪表板中对可解释AI的启发性评估
Bethany A van Dort1, Jarell López Cañizares1, Romaric Marcilly2
1Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, eHealth Living and Learning Lab, Amsterdam, The Netherlands.
Studies in health technology and informatics
|May 17, 2025
概括
这项研究评估了一种可解释的人工智能 (XAI) 仪表板,用于治疗败血症相关 Delirium (SAD). SAD XAI仪表板显示了可用性差距,影响了临床决策支持的信任和透明度.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 可解释的人工智能 (XAI) 对于医疗保健中的透明和可解释的人工智能至关重要.
- SAD XAI仪表板是治疗败血症相关妄想 (SAD) 的临床决策支持工具.
研究的目的:
- 评估SAD XAI仪表板是否符合已建立的XAI可用性启发式.
- 识别影响医生对SAD人工智能预测的理解和信任的可用性差距.
主要方法:
- 三位人类因素和健康信息学专家使用XAI可用性启发式检查清单评估了仪表板.
- 评估的重点是透明度,可解释性,可操作性和整体可用性.
主要成果:
- 发现了一些可用性问题,包括不清楚的动作序列,非标准图标和不一致的标签.
- 与信任和透明度相关的关键启发式信息显然不在仪表板设计中.
结论:
- 在SAD XAI仪表板上显示了重要的可用性差距,需要解决才能有效实施.
- 未来的XAI仪表板开发必须优先考虑可用性启发式,特别是那些促进信任和透明度的启发式,以增强最终用户的采用.
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