  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.
