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

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.

[1]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[3]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[4]  Patrick Shafto,et al.  Mitigating belief projection in explainable artificial intelligence via Bayesian teaching , 2021, Scientific reports.

[5]  Aki Vehtari,et al.  Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC , 2015, Statistics and Computing.

[6]  David Gunning,et al.  DARPA's explainable artificial intelligence (XAI) program , 2019, IUI.

[7]  Noah D. Goodman,et al.  A rational account of pedagogical reasoning: Teaching by, and learning from, examples , 2014, Cognitive Psychology.

[8]  F. Sardanelli,et al.  Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States , 2018, Insights into Imaging.

[9]  Isaac S Kohane,et al.  Artificial Intelligence in Healthcare , 2019, Artificial Intelligence and Machine Learning for Business for Non-Engineers.

[10]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[11]  Tim Miller,et al.  Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences , 2017, ArXiv.

[12]  Noah D. Goodman,et al.  Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations , 2008 .

[13]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[14]  F. Sardanelli,et al.  Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine , 2018, European Radiology Experimental.

[15]  Charlene Liew.  The future of radiology augmented with Artificial Intelligence: A strategy for success. , 2018, European journal of radiology.

[16]  E. Topol,et al.  A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. , 2019, The Lancet. Digital health.

[17]  Kush R. Varshney,et al.  On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products , 2016, Big Data.

[18]  Emily Chen,et al.  How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation , 2018, ArXiv.

[19]  Patrick Shafto,et al.  Toward a general, scaleable framework for Bayesian teaching with applications to topic models , 2016, ArXiv.

[20]  Thomas L. Griffiths,et al.  Infant directed speech is consistent with teaching , 2016, Psychological review.

[21]  E. Topol,et al.  Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. , 2016, JAMA.