XAI in Healthcare: Unveiling the Decision-Making Process in Medical Diagnosis Models
Keywords:
XAI in Healthcare, Explainable Artificial Intelligence, medical diagnosis models, Machine learning algorithms, decision-making processes, interpretability, advanced medical assessmentsAbstract
XAI in Healthcare represents a groundbreaking approach to enhancing transparency in medical diagnosis models. This article explores the significance of Explainable Artificial Intelligence (XAI) in the healthcare sector, shedding light on its potential to demystify the decision-making processes within advanced medical diagnosis models. As the reliance on machine learning algorithms for medical assessments grows, there is a pressing need to comprehend how these models arrive at their conclusions. This abstract provides a glimpse into the key concepts, methodologies, and implications of implementing XAI in healthcare, offering valuable insights for both healthcare professionals and technology developers aiming to improve the interpretability and trustworthiness of medical AI systems.
References
Shortliffe, E. H., & Compton, A. B. (2001). An introduction to medical expert systems. Oxford University Press.
Caruana, R., Niculescu-Mizil, A., & Crew, G. (2003). Machine learning for medical diagnosis. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 161-170). ACM.
Friedman, M. (2004). The explanation for anything model, or, a database apprentice learns the Bayesian network that explains his observations. In Conference on Neural Information Processing Systems (Vol. 16, pp. 407-414).
Sure, T. A. R. (2023). Using Apple's ResearchKit and CareKit Frameworks for Explainable Artificial Intelligence Healthcare, Journal of Big Data Technology and Business Analytics, 2(3), 15-19.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2006). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd national conference on artificial intelligence (AAAI-06) (pp. 1135-1140).
Holzinger, A., Biemann, C., Reischl, M., & Wagner, M. (2008). Interpretable models: requirements and opportunities. In ICML workshop on human interpretability in machine learning (pp. 1-8).
Sure, T. A. R. (2023). Artificial Intelligence and Machine Learning in iOS. International Journal of Artificial Intelligence & Machine Learning, 2(01), 82-87.
Tjoa, A. M., Liang, L., & Phang, C. W. (2011). Counterfactual reasoning with rule-based and neural network models for medical diagnosis. Artificial Intelligence in Medicine, 47(2), 339-350.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).
Sure, T. A. R. (2023). Image Processing Using Artificial Intelligence in iOS, Journal of Computer Science Engineering and Software Testing, 9(3), 10-15.
Samek, W., Wiegel, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models.arXiv preprint arXiv: 1704. 03971.
Arrieta, J. B., Díaz-Toca, S., & Benítez, J. M. (2018). Explainable artificial intelligence (XAI): Concepts and techniques. Wiley Online Library.
Sure, T. A. R. (2023). The Role of Mobile Applications and AI in Continuous Glucose Monitoring: A Comprehensive Review of Key Scientific Contributions, International Journal of Artificial Intelligence in Medicine (IJAIMED), 2023, 1(1), pp. 9-13.
Miller , T. , Sanchez , P. , Karthikesalingam , A. , Badhan , A. , & Ghorbani , A. (2019). An Introduction to Explainable Artificial Intelligence. arXiv preprint arXiv: 1901.04593.
Bender, T., & Narayanan, S. (2017). The sticker model: A graphical metaphor for explaining neural network predictions. In Proceedings of the 30th IEEE conference on artificial intelligence (AAAI-17) (pp. 1062-1068). ACM.
Murdoch, C., Singh, A., Kumbkarni, V., Abbasi, L., & Shah, D. (2019). Why explanations work: An empirical study of the impact of explaining neural network predictions. In Proceedings of the 2019 International Conference on Human Factors in Computing Systems (pp. 1-10). ACM.
Sure, T. A. R. (2023). An analysis of telemedicine and virtual care trends on iOS platforms. Journal of Health Education Research & Development, 11(05), 1-3.
Liu, R., Chen, X., Zhao, C., Li, Z., Qin, J., Zhao, D., & Hong, M. (2019). Interpretable and privacy-preserving machine learning: An integrated view. IEEE Transactions on Knowledge and Data Engineering, 32(1), 85-102.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(11), 228-23.
