基于证据的XAI:一种经验方法来设计更有效和可解释的决策支持系统
Lorenzo Famiglini1, Andrea Campagner2, Marilia Barandas3
1Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Computers in biology and medicine
|February 3, 2024
概括
在人工智能辅助的断裂检测中,类激活地图 (CAM) 在突出较低级别的特征和使用传统配色时表现最好. 这项用户研究揭示了改善诊断准确性和放射学任务中的用户满意度的见解.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 类激活地图 (CAM) 在可解释AI (XAI) 中用于放射性诊断.
- 评估CAM对诊断准确度和用户满意度的影响至关重要.
- 在CAM中,颗粒度和颜色等特征需要进一步调查.
研究的目的:
- 评估CAMs颗粒度和颜色对胸脊柱骨折 (TL) 检测的影响.
- 评估CAMs对诊断准确性,信心和实用性的影响.
- 调查案例复杂性,人工智能准确性和用户专业知识如何影响CAM的有效性.
主要方法:
- 进行了一项用户研究,涉及放射性诊断任务 (TL骨折检测).
- 操纵了两个CAM特征:特征级别 (较低与较高) 和配色方案 (语义与传统).
- 测量了诊断准确度,感知信心和实用性.
主要成果:
- 低级特征CAMs提高了诊断准确性,特别是对于经验丰富的医生.
- 传统的配色方案在诊断准确性方面始终优于语义色彩.
- 这些发现挑战了XAI领域关于CAM设计的现有假设.
结论:
- 设计有效的XAI工具需要基于证据,以人为中心的方法.
- 在TL骨折检测中,为CAMs推使用较低级别的特征和传统颜色.
- 为选择和评估XAI解决方案提出了证据框架的层次结构.
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