Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 8 Jun 2021.
Builds on
10 of 26 references.
References · 10
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
- 2015Practical Bayesian model evaluation using leave-one-out cross-validation and WAICcited by 110
- 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
- 2018How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanationcited by 71
- 2016On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Productscited by 52
- 2021Mitigating belief projection in explainable artificial intelligence via Bayesian Teachingcited by 10
- 2016Infant directed speech is consistent with teachingcited by 5
- 2016Toward a general, scaleable framework for Bayesian teaching with applications to topic modelscited by 4
Built on by
Citing papers per year: 2021: 2.
2 papers.