Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching

Tomas Folke; Scott Cheng-Hsin Yang; Sean Anderson; Patrick Shafto

doi:10.1117/12.2585967

12 April 2021 Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching

Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson, Patrick Shafto

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462J (2021) https://doi.org/10.1117/12.2585967
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson, and Patrick Shafto "Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462J (12 April 2021); https://doi.org/10.1117/12.2585967

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