Interpretable AI and ML Models for Transparent Clinical Decision-Making
DOI:
https://doi.org/10.5281/zenodo.20443300Keywords:
Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Explainable Artificial Intelligence (XAI), Model Transparency in Medicine, Interpretability of Machine Learning Models, Accountability in Clinical AI, Risk Prediction and Prognostic Modeling, Black-Box Model Limitations, Human-Centered AI Design, Trustworthy Medical AI, Ethical AI in Healthcare, Model Explainability Frameworks, Data-Driven Clinical Risk Assessment, AI Governance in Health Systems.Abstract
Artificial intelligence has the potential to augment clinical decision making. By learning patterns
of risk and disease directly from empirical data, AI methods offer one solution to the difficulty
health care professionals face in considering ever-increasing amounts of information. Clinicians
making a medical decision for a patient want not only an accurate estimate of the risks associated
with their patient's disease or treatment options but also an understanding of the reasoning
behind these risks. This desire for explanation drives the growing interest in explainability in AI,
particularly in AI for health care.
Explainable Artificial Intelligence (XAI) is defined as methods that generate new AI models for
which the behaviour can be understood, directly or indirectly, by humans. The concept of human
understanding encompasses three different levels – transparency, interpretability and
accountability. The heart of the concern for transparency in AI is the incomprehensibility of the
learned representations, the “black box” nature of the complex function learned from the training
data.
References
[1] Adadi, A., & Berrada, M. (2020). Peeking inside the black-box: A survey on explainable
artificial intelligence (XAI). IEEE Access, 8, 52138–52160.
[2] Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in
healthcare. Proceedings of the 2018 ACM International Conference on Bioinformatics,
Computational Biology, and Health Informatics, 559–560.
[3] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable Artificial
Intelligence (XAI): Concepts, taxonomies, opportunities and challenges. Information Fusion,
58, 82–115.
[4] Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care.
JAMA, 319(13), 1317–1318.
[5] Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics.
Management Science, 66(3), 1025–1044.
[6] Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis. CRC Press.
[7] Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning
interpretability. Electronics, 8(8), 832.
[8] Chen, I. Y., Joshi, S., Ghassemi, M., & Ranganath, R. (2021). Probabilistic machine
learning for healthcare. Annual Review of Biomedical Data Science, 4, 393–419.
[9] Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., et al. (2018). Opportunities and
obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface,
15(141), 20170387.
[10] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine
learning. arXiv preprint.
[11] Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in
healthcare. Nature Medicine, 25(1), 24–29.
[12] Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current
approaches to explainable AI in health care. The Lancet Digital Health, 3(11), e745–e750.
[13] Gilpin, L. H., Bau, D., Yuan, B. Z., et al. (2018). Explaining explanations: An overview
of interpretability of machine learning. Proceedings of the IEEE, 106(8), 1178–1194.
[14] Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and
explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery, 9(4), e1312.
[15] Holzinger, A., Carrington, A., & Müller, H. (2020). Measuring the quality of
explanations. Artificial Intelligence and Law, 28, 193–198.
[16] Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare. Stroke and
Vascular Neurology, 2(4), 230–243.
[17] Johnson, A. E. W., Pollard, T. J., Shen, L., et al. (2016). MIMIC-III. Scientific Data, 3,
160035.
[18] Johnson, A. E. W., Stone, D. J., Celi, L. A., & Pollard, T. J. (2021). MIMIC-IV.
Scientific Data, 8, 257.
[19] Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The
artificial intelligence clinician. Nature Medicine, 24(11), 1716–1720.
[20] Kundu, S. (2021). AI in medicine must be explainable. Nature Medicine, 27, 1328.
[21] Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the
ACM, 61(10), 36–43.
[22] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model
predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
[23] Molnar, C. (2022). Interpretable machine learning (2nd ed.). Lulu.
[24] Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and
understanding deep neural networks. Digital Signal Processing, 73, 1–15.
[25] Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine
learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
[26] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias
in an algorithm. Science, 366(6464), 447–453.
[27] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Proceedings of
KDD, 1135–1144.
[28] Rudin, C. (2019). Stop explaining black box machine learning models. Nature Machine
Intelligence, 1, 206–215.
[29] Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K. R. (2019).
Explainable AI: Interpreting, explaining and visualizing deep learning. Springer.
[30] Saria, S., & Subbaswamy, A. (2019). Tutorial: Safe and reliable machine learning. arXiv
preprint.
[31] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR. IEEE Journal of
Biomedical and Health Informatics, 22(5), 1589–1604.
[32] Shortliffe, E. H., & Cimino, J. J. (2021). Biomedical informatics (5th ed.). Springer.
[33] Sittig, D. F., & Singh, H. (2016). A socio-technical approach. Journal of the American
Medical Informatics Association, 23(4), 641–647.
[34] Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What clinicians
want. npj Digital Medicine, 2, 1–7.
[35] Topol, E. (2019). Deep medicine. Basic Books.
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