专家目光作为医疗AI决策支持系统的可用性指标:一项初步研究
Nora Castner1,2, Lubaina Arsiwala-Scheppach3, Sarah Mertens3
1Carl Zeiss Vision International GmbH, Tübingen, Germany. nora.castner@zeiss.com.
NPJ digital medicine
|July 27, 2024
概括
这项研究使用眼睛跟踪来评估医疗人工智能 (AI) 在牙科放射学中的可用性. 研究结果表明,人工智能使用可能会破坏专家的视觉工作流程,这表明需要改进设计以更好地实现临床整合.
科学领域:
- 医疗人工智能 医疗人工智能
- 人与计算机的交互
- 牙科放射学 牙科放射学
背景情况:
- 集成人工智能 (AI) 的协作系统在临床工作流程中越来越受欢迎.
- 了解专家与医疗AI的互动对于优化系统设计至关重要.
- 眼睛跟踪指标为评估AI可用性提供了强有力的指标.
研究的目的:
- 评估医疗人工智能支持系统的可用性和用户互动,以检测.
- 在与人工智能辅助牙科放射学交互时调查专家的凝视行为.
- 为在临床环境中设计以用户为中心的AI系统提供见解.
主要方法:
- 采用眼睛跟踪来分析专家的凝视模式,在咬翼X射线图像中检测虫.
- 将AI支持的查看 (开启/关闭交换) 与没有AI的标准查看进行比较.
- 评估了与人工智能软件的相互作用,用于在牙科放射图中检测.
主要成果:
- 专家们在总检查时间的约25%使用了AI支持,通常在中间激活它.
- 人工智能使用导致人们对人工智能相关的用户界面元素的关注增加,注意力更频繁地转移.
- 这些注意力转移可能会增加整体评估时间,可能会影响效率.
结论:
- 凝视分析揭示了对人工智能用于医学图像检查的可用性有价值的见解.
- 当前的人工智能交互模式可能会破坏优化的专家视觉策略.
- 需要进一步的研究来开发AI在临床实践中的可用性指标.
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