Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts’ interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI’s usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.

鉴于医疗人工智能 (AI) 的现状及其认知，协作系​​统正在成为临床工作流程的首选。这项工作旨在解决专家与医疗人工智能支持系统的交互问题，以深入了解如何更好地考虑用户的需求来设计这些系统。由于眼动追踪指标已被证明是稳健的可用性指标，因此我们利用它们来评估可用性以及用户与医疗人工智能支持系统的交互。我们使用专家凝视来评估专家与 AI 软件的交互，以在咬翼 X 射线图像中进行龋齿检测。我们将没有 AI 支持的咬翼图像的标准查看与可以自由打开和关闭 AI 支持的查看进行了比较。我们发现，专家在整个检查任务中开启人工智能的时间大约为 25%，并且通常在检查过程中途开启。凝视行为表明，当人工智能支持时，更多的注意力集中在与人工智能支持相关的用户界面元素上，从图像本身到这些元素的转换更加频繁。考虑到专家视觉策略已经针对快速有效的图像检查进行了优化，这种注意力中断可能会导致整体评估所需的时间增加。视线分析为人工智能在医学图像检查中的可用性提供了宝贵的见解。应该对这些工具以及如何描述可用性的度量进行进一步分析。