Challen, R. et al. Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 28, 231–237 (2019).
Google Scholar
Hendrycks, D. & Gimpel, K. A baseline for detecting misclassified and out-of-distribution examples in neural networks. Preprint at arXiv https://arxiv.org/abs/1610.02136 (2018).
Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and harnessing adversarial examples. Preprint at arXiv https://arxiv.org/abs/1412.6572 (2015).
Amodei, D. et al. Concrete problems in AI safety. Preprint at arXiv https://arxiv.org/abs/1606.06565 (2016).
Nguyen, A., Yosinski, J. & Clune, J. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 427–436 (2015).
He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25, 30–36 (2019).
Google Scholar
Kompa, B., Snoek, J. & Beam, A. L. Second opinion needed: communicating uncertainty in medical machine learning. NPJ Digit. Med. 4, 4 (2021).
Google Scholar
Guo, C., Pleiss, G., Sun, Y. & Weinberger, K. Q. On calibration of modern neural networks. In Proc. 34th Int. Conference on Machine Learning (PMLR) 70, 1321–1330 (2017).
Dyer, T. et al. Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm. Clin. Radiol. 76, 473–473 (2021).
Google Scholar
Dyer, T. et al. Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans. Neuroradiology 64, 735–743 (2022).
Google Scholar
Liang, X., Nguyen, D. & Jiang, S. B. Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion. Mach. Learn. Sci. Technol. 2, 015007 (2020).
Navarrete-Dechent, C. et al. Automated dermatological diagnosis: hype or reality? J. Invest. Dermatol. 138, 2277–2279 (2018).
Google Scholar
Krois, J. et al. Generalizability of deep learning models for dental image analysis. Sci. Rep. 11, 6102 (2021).
Google Scholar
Sathitratanacheewin, S., Sunanta, P. & Pongpirul, K. Deep learning for automated classification of tuberculosis-related chest X-ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon 6, e04614 (2020).
Google Scholar
Xin, K. Z., Li, D. & Yi, P. H. Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data. Emerg. Radiol. 29, 107–113 (2022).
Google Scholar
Zech, J. R. et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15, e1002683 (2018).
Google Scholar
Chen, J. S. et al. Deep learning for the diagnosis of stage in retinopathy of prematurity: accuracy and generalizability across populations and cameras. Ophthalmol. Retina 5, 1027–1035 (2021).
Google Scholar
Jiang, H., Kim, B., Guan, M. & Gupta, M. To trust or not to trust a classifier. In Advances in Neural Information Processing Systems 31 (2018).
Geifman, Y. & El-Yaniv, R. Selectivenet: a deep neural network with an integrated reject option. In Proc. 36th Int. Conference on Machine Learning (PMLR) 97, 2151–2159 (2019).
Madras, D., Pitassi, T. & Zemel, R. Predict responsibly: improving fairness and accuracy by learning to defer. In Advances in Neural Information Processing Systems 31 (2018).
Kim, D. et al. Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model. Nat. Commun. 13, 1867 (2022).
Google Scholar
Bernhardt, M. et al. Active label cleaning for improved dataset quality under resource constraints. Nat. Commun. 13, 1161 (2022).
Google Scholar
Krause, J. et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125, 1264–1272 (2018).
Google Scholar
Basha, S. H. S., Dubey, S. R., Pulabaigari, V. & Mukherjee, S. Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing 378, 112–119 (2020).
Google Scholar
Trabelsi, A., Chaabane, M. & Ben-Hur, A. Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities. Bioinformatics 35, i269–i277 (2019).
Google Scholar
Boland, G. W. L. Voice recognition technology for radiology reporting: transforming the radiologist’s value proposition. J. Am. Coll. Radiol. 4, 865–867 (2007).
Google Scholar
Heleno, B., Thomsen, M. F., Rodrigues, D. S., Jorgensen, K. J. & Brodersen, J. Quantification of harms in cancer screening trials: literature review. BMJ 347, f5334–f5334 (2013).
Google Scholar
Dans, L. F., Silvestre, M. A. A. & Dans, A. L. Trade-off between benefit and harm is crucial in health screening recommendations. Part I: general principles. J. Clin. Epidemiol. 64, 231–239 (2011).
Google Scholar
Peryer, G., Golder, S., Junqueira, D. R., Vohra, S. & Loke, Y. K. in Cochrane Handbook for Systematic Reviews of Interventions (eds Higgins, J. P. et al.) Ch. 19, 493–505 (John Wiley & Sons, 2011).
Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H. S. & Gal, Y. Deep deterministic uncertainty: a simple baseline. Preprint at arXiv https://arxiv.org/abs/2102.11582 (2022).
Kruschke, J. K. in The Cambridge Handbook of Computational Psychology (ed. Sun, R.) 267–301 (Cambridge Univ. Press, 2008).
Bowman, C. R., Iwashita, T. & Zeithamova, D. Tracking prototype and exemplar representations in the brain across learning. eLife 9, e59360 (2020).
Google Scholar
Platt, J. C. in Advances in Large Margin Classifiers (eds Smola, A. J. et al.) (MIT Press, 1999).
Ding, Z., Han, X., Liu, P. & Niethammer, M. Local temperature scaling for probability calibration. In Proc. IEEE/CVF International Conference on Computer Vision 6889–6899 (2021).
Clinciu, M.-A. & Hastie, H. A survey of explainable AI terminology. In Proc. 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI) 8–13 (2019).
Biran, O. & Cotton, C. Explanation and justification in machine learning: a survey. In IJCAI-17 Workshop on Explainable Artificial Intelligence (XAI) 8, 8–13 (2017).