  Limited expert time is a key bottleneck in medical imaging. Due to advances
in image classification, AI can now serve as decision-support for medical
experts, with the potential for great gains in radiologist productivity and, by
extension, public health. However, these gains are contingent on building and
maintaining experts' trust in the AI agents. Explainable AI may build such
trust by helping medical experts to understand the AI decision processes behind
diagnostic judgements. Here we introduce and evaluate explanations based on
Bayesian Teaching, a formal account of explanation rooted in the cognitive
science of human learning. We find that medical experts exposed to explanations
generated by Bayesian Teaching successfully predict the AI's diagnostic
decisions and are more likely to certify the AI for cases when the AI is
correct than when it is wrong, indicating appropriate trust. These results show
that Explainable AI can be used to support human-AI collaboration in medical
imaging.
