Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.

翻译：人工智能（AI）为支持病理学家在高分辨率肿瘤图像中导航、搜索感兴趣的病理模式带来了进步。然而，现有的AI辅助工具尚未实现这一预期潜力，原因在于缺乏对病理学实践的洞察以及人机交互（HCI）对病理学家导航工作流的考虑。我们首先对六位病理学领域的医疗专业人员进行了一项形成性研究，以捕捉他们的导航策略。通过结合我们的观察以及病理学家的领域知识，我们设计了NaviPath——一个面向病理学家的AI协同导航系统。一项对15位病理学领域医疗专业人员的评估研究表明：（i）与手动导航相比，参与者使用NaviPath在单位时间内发现的病理模式数量超过两倍；（ii）参与者在精确率和召回率方面平均表现优于纯AI和手动导航。进一步的定性分析显示，NaviPath下的导航具有更高的一致性，这有助于提升整体检查质量。