可解释智能系统的设计空间概述
Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis
摘要：
智能系统的采用为医疗工作带来了机遇和挑战。从积极的方面来看，智能系统有可能计算来自患者的复杂数据，并为医生生成自动诊断建议。然而，医疗专业人员通常将这种系统视为黑盒，因此，对依赖系统生成的结果来做出决策感到担忧。在本文中，我们有助于正在进行的讨论可解释的人工智能（XAI）从以人为中心的角度探索解释的概念。我们假设，如果系统被设计成像医生一样思考和行动，医疗专业人员会认为系统是可解释的。我们报告了一项初步的访谈研究，收集了六名医疗专业人员对他们如何与诊断和治疗目的的数据进行互动的反映。我们的数据揭示了医生何时以及如何在各种类型的数据中进行优先排序，作为其诊断过程的核心部分。基于这些发现，我们概述了未来的方向，在医疗方面的XAI系统的设计。
英文摘要：
The adoption of intelligent systems creates opportunities as well as challenges for medical work. On the positive side, intelligent systems have the potential to compute complex data from patients and generate automated diagnosis recommendations for doctors. However, medical professionals often perceive such systems as black boxes and, therefore, feel concerned about relying on system generated results to make decisions. In this paper, we contribute to the ongoing discussion of explainable artificial intelligence (XAI) by exploring the concept of explanation from a human-centered perspective. We hypothesize that medical professionals would perceive a system as explainable if the system was designed to think and act like doctors. We report a preliminary interview study that collected six medical professionals' reflection of how they interact with data for diagnosis and treatment purposes. Our data reveals when and how doctors prioritize among various types of data as a central part of their diagnosis process. Based on these findings, we outline future directions regarding the design of XAI systems in the medical context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
