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The adoption of intelligent systems creates opportunities as well aschallenges for medical work. On the positive side, intelligent systems have thepotential to compute complex data from patients and generate automateddiagnosis recommendations for doctors. However, medical professionals oftenperceive such systems as black boxes and, therefore, feel concerned aboutrelying on system generated results to make decisions. In this paper, wecontribute to the ongoing discussion of explainable artificial intelligence(XAI) by exploring the concept of explanation from a human-centeredperspective. We hypothesize that medical professionals would perceive a systemas explainable if the system was designed to think and act like doctors. Wereport 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 asa central part of their diagnosis process. Based on these findings, we outlinefuture directions regarding the design of XAI systems in the medical context.

Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis