Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis
By Yao Xie, Ge Gao, Xiang 'Anthony' Chen. 2019
45 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Informatics
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Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “More specifically, it is pointed out that interpretabilities in the medical fields include factors other fields do not consider, including risk and responsibilities [15] [91] [92].”