论文标题
CHEXPLAIN：使医生能够探索和理解以AI为启用的Data驱动的医学成像分析
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis
论文作者
论文摘要
数据驱动的AI的最新发展有望自动化医学诊断；但是，大多数AI充当有限的计算知识的医生的“黑匣子”。我们将医学成像作为出发点，我们进行了三个设计活动的迭代，以制定构造构造 - 一种使医生能够探索和理解AI支持AI支持的胸部X射线分析的系统：（1）参考医师和放射线医师之间的配对调查揭示是否需要什么解释以及需要什么样的解释； （2）与三位医生共同设计的低保真原型构成了八个关键特征； （3）由另外六个医生评估的高保真原型提供了有关如何使AI探索和理解AI的详细总结见解。我们通过讨论未来工作的建议来总结，以设计和实施可解释的医学AI系统，该系统包括四个反复出现的主题：动机，约束，解释和理由。
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.
