Optimizing Healthcare Decisions Using Explainable AI for Enhanced Predictions
DOI:
https://doi.org/10.54489/c6nyem73Keywords:
Explainable AI, Healthcare, Transparency, Interpretability, LIME, SHAPAbstract
In the last few years, the use of AI in the healthcare sector has brought about a great change where decision-making processes are concerned, and thus the accuracy of diagnosis, treatment planning, and patient outcomes has improved by leaps and bounds. This article investigates the application of Explainable AI (XAI) in the optimization of health decisions, pointing out the importance of interpretability and transparency in AI models that are used for more accurate prognoses. Usually, traditional AI tools which are commonly known as "black boxes" are the cause of disconnection among medical practitioners because the real decision-making process is not clear. In contrast, the XAI approach provides understandable insights by enabling users to understand the model's actions, therefore, it builds confidence and helps users make informed choices. This paper will cover the different XAI strategies ranging from computer vision-based ones to those expanding on its application in healthcare, besides tackling questions of how they affect prediction accuracy and reliability of health. It will also include case studies on successful XAI implementation. Also, the ethical issues and the development of the future are used to address the issue so as to ensure that healthcare does not just improve performance, but also is in line with the patient-oriented and regulatory standards. Let us through this exploration, show how XAI can be a spark of hope for the advancement of healthcare delivery, as well as an enabler of more transparency, accountability, and effectiveness in the healthcare "ecosystem".
References
Keleko, A.T., Kamsu-Foguem, B., Ngouna, R.H. and Tongne, A., 2023. Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI. Advances in Engineering Software, 175, p.103339..
Sadeghi, Z., Alizadehsani, R., Cifci, M.A., Kausar, S., Rehman, R., Mahanta, P., Bora, P.K., Almasri, A., Alkhawaldeh, R.S., Hussain, S. and Alatas, B., 2023. A Brief Review of Explainable Artificial Intelligence in Healthcare. arXiv preprint arXiv:2304.01543.
Cina, G., Rober, T.E., Goedhard, R. and Birbil, S.I., 2023, June. Semantic match: Debugging feature attribution methodstitlebreak in XAI for healthcare. In Conference on Health, Inference, and Learning (pp. 182-190). PMLR.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
Marzec, M. L., & Austin, R. E. (2018). Ensuring transparency, fairness, and effectiveness in machine learning-based clinical decision support systems. Journal of the American Medical Informatics Association, 25(12), 1681-1685.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
Stacey, D., Légaré, F., Col, N. F., Bennett, C. L., Barry, M. J., Eden, K. B., ... & Wu, J. H. (2017). Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, 4(4), CD001431.
Wang, F., & Rudin, C. (2015). Falling rule lists. In International Conference on Machine Learning (pp. 1019-1028).
Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517-518.
Solís-Martín, D., Galán-Páez, J. and Borrego-Díaz, J., 2023. On the Soundness of XAI in Prognostics and Health Management (PHM). Information, 14(5), p.256.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2019). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866-872.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
Holzinger, A., Langs, G., Denk, H., & Zatloukal, K. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1721-1730).
Bharati, S., Mondal, M.R.H. and Podder, P., 2023. A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?. IEEE Transactions on Artificial Intelligence.
Chen, J. H., Asch, S. M., & Machine Learning and the Science of Persuasion. JAMA, 320(22), 2273-2274.
Zhang, Y., Zhan, S., & Barman, A. (2019). A hybrid approach of deep learning and rule-based reasoning for credit risk evaluation. Expert Systems with Applications, 125, 253-264.
Javed, A.R., Khan, H.U., Alomari, M.K.B., Sarwar, M.U., Asim, M., Almadhor, A.S. and Khan, M.Z., 2023. Toward explainable AI-empowered cognitive health assessment. Frontiers in Public Health, 11, p.1024195.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Liu, X., Faes, L., & Kale, A. U. (2020). A comparison of deep learning models for the diagnosis of age-related macular degeneration. IEEE Journal of Biomedical and Health Informatics, 24(12), 3535-3544.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.
Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 30-57.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Liu, P. J. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18.
Du, S., Wang, W., & Qiu, S. (2020). Towards interpretable deep learning for COVID-19 detection via visual explanation. Pattern Recognition Letters, 138, 389-395.
Islam, M. R., Shah, N., & Zhang, Y. (2020). Explainable artificial intelligence in healthcare: A comprehensive survey. Artificial Intelligence Review, 53(4), 2265-2313.
Gunning, D. (2017). Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA).
Carvalho, A., Freitas, A., & Oliveira, A. L. (2019). A hybrid deep learning model for disease prediction using electronic health records. IEEE Access, 7, 95129-95141.
Wiens, J., Saria, S., Sendak, M., & Ghassemi, M. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340.
Ong, E., Wong, Y. H., & Goh, G. B. (2020). Combining deep learning and saliency map for breast cancer prediction using mammograms. Journal of Medical Systems, 44(2), 1-12.
Cabitza, F., Rasoini, R., & Gensini, G. F. (2019). Unintended consequences of machine learning in medicine. JAMA, 322(6), 517-518.
