The recent development of data-driven AI promises to automate medical diagnosis; however, most AI functions as 'black boxes' to physicians with limited computational knowledge. Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain---a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: (1) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (2) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (3) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI. We summarize by discussing recommendations for future work to design and implement explainable medical AI systems that encompass four recurring themes: motivation, constraint, explanation, and justification.

中文翻译：

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CheXplain：使医生能够探索和理解数据驱动、人工智能支持的医学成像分析

数据驱动的人工智能的最新发展有望实现医疗诊断的自动化；然而，对于计算知识有限的医生来说，大多数人工智能都是“黑匣子”。以医学影像为出发点，我们进行了三轮设计活动，以制定 CheXplain——一个使医生能够探索和理解 AI 支持的胸部 X 射线分析的系统：(1) 转诊医生和医生之间的配对调查。放射科医生揭示是否、何时以及需要何种解释；(2) 与三位医师共同设计的低保真原型制定了八个关键特征；(3) 由另外六名医生评估的高保真原型提供了关于每个特征如何实现对人工智能的探索和理解的详细总结性见解。