Explainable AI and ML Models for Transparent Clinical Decision Support
Main Article Content
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.
Article Details
References
Adadi, A., & Berrada, M. (2020). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 8, 52138–52160.
Goutham Kumar Sheelam, Hara Krishna Reddy Koppolu. (2022). Data Engineering And Analytics For 5G-Driven Customer Experience In Telecom, Media, And Healthcare. Migration Letters, 19(S2), 1920–1944. Retrieved from https://migrationletters.com/index.php/ml/article/view/11938
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.
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318.
Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis. CRC Press.
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability. Electronics, 8(8), 832.
Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932
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.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint.
Koppolu, H. K. R., Sheelam, G. K., & Komaragiri, V. B. (2023). Autonomous Telecommunication Networks: The Convergence of Agentic AI and AI-Optimized Hardware. International Journal of Science and Research (IJSR), 12(12), 2253-2270.
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.
Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
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.
Guntupalli, R. (2023). Optimizing Cloud Infrastructure Performance Using AI: Intelligent Resource Allocation and Predictive Maintenance. Available at SSRN 5329154.
Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare. Stroke and Vascular Neurology, 2(4), 230–243.
Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609
Johnson, A. E. W., Stone, D. J., Celi, L. A., & Pollard, T. J. (2021). MIMIC-IV. Scientific Data, 8, 257.
Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
Kundu, S. (2021). AI in medicine must be explainable. Nature Medicine, 27, 1328.
Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43.
Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
[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] AI Powered Fraud Detection Systems: Enhancing Risk Assessment in the Insurance Sector. (2023). American Journal of Analytics and Artificial Intelligence (ajaai) With ISSN 3067-283X, 1(1). https://ajaai.com/index.php/ajaai/article/view/14
[26] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm. Science, 366(6464), 447–453.
[27] Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
[28] Rudin, C. (2019). Stop explaining black box machine learning models. Nature Machine Intelligence, 1, 206–215.
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K. R. (2019). Explainable AI: Interpreting, explaining and visualizing deep learning. Springer.
Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
Sittig, D. F., & Singh, H. (2016). A socio-technical approach. Journal of the American Medical Informatics Association, 23(4), 641–647.
Meda, R. (2023). Developing AI-Powered Virtual Color Consultation Tools for Retail and Professional Customers. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3577.
Topol, E. (2019). Deep medicine. Basic Books.
Van der Schaar, M., Alaa, A. M., Floto, A., et al. (2021). How machine learning can help healthcare systems. Machine Learning, 110(1), 1–20.
Wiens, J., Saria, S., Sendak, M., et al. (2019). Do no harm. Nature Medicine, 25(9), 1337–1340.
Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
Wehbe, R. M., et al. (2021). Deep learning in clinical NLP. Journal of the American Medical Informatics Association, 28(2), 1–15.
Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.
Zhou, S., et al. (2023). A survey of explainable artificial intelligence in healthcare. Artificial Intelligence in Medicine, 138, 102473.
Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment. (2023). American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140) , 1(1). https://aojse.com/index.php/aojse/article/view/19
Choudhury, A., & Naumann, F. (2022). Interpretable ML in healthcare. IEEE Access, 10, 104541–104557.
McCradden, M. D., Joshi, S., Anderson, J. A., et al. (2020). Patient safety and quality. npj Digital Medicine, 3, 1–5.
Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
Björck, J., et al. (2021). Neural networks with monotonicity constraints. Proceedings of ICML.
Caruana, R., et al. (2015). Intelligible models for healthcare. Proceedings of KDD, 1721–1730.
Koh, P. W., & Liang, P. (2017). Influence functions. Proceedings of ICML, 1885–1894.
Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 1(1).
Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine. Annals of Internal Medicine, 167(3), 219–220.
Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
Rajkomar, A., et al. (2018). Scalable and accurate deep learning with EHRs. npj Digital Medicine, 1, 18.
Ramesh Inala. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493–5505. https://doi.org/10.53555/kuey.v29i4.10424
Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
Kumar Bandi, V. D. V. (2023). MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms. Journal of Artificial Intelligence and Big Data, 3(1), 81–101. Retrieved from https://www.scipublications.com/journal/index.php/jaibd/article/view/1368
Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 444–455. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7905.
Guidotti, R., et al. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42.
Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
Raji, I. D., et al. (2020). Closing the AI accountability gap. Proceedings of FAT*, 33–44.
Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
Buolamwini, J., & Gebru, T. (2018). Gender shades. Proceedings of FAT*, 77–91.
European Commission. (2021). Ethics guidelines for trustworthy AI.
Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31..
Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78. https://doi.org/10.38124/ijsrmt.v2i12.1236
Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774
U.S. Food and Drug Administration. (2021). Artificial intelligence/machine learning software as a medical device.
Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256
Kummari, D. N. (2023). Energy Consumption Optimization in Smart Factories Using AI-Based Analytics: Evidence from Automotive Plants. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3572.
Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
Zhang, Q., et al. (2021). Interpreting deep learning models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3378–3395.
Tonekaboni, S., et al. (2021). Clinician-centered explainable AI. Nature Machine Intelligence, 3, 40–47.
Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
Louizos, C., et al. (2018). Causal effect inference. NeurIPS.
Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference. MIT Press.
Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1360
Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
Ribeiro, M. T., et al. (2018). Anchors. Proceedings of AAAI.
Wang, C., et al. (2022). Explainable boosting machines for healthcare. Artificial Intelligence in Medicine, 126, 102187.
Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1–17. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1352
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.
Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.
Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.
Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111
Lundberg, S. M., et al. (2020). From local explanations to global understanding. Nature Machine Intelligence, 2, 252–259.
Kummari, D. N. (2023). AI-Powered Demand Forecasting for Automotive Components: A Multi-Supplier Data Fusion Approach. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN 3050-9734 en e-ISSN 3050-9742, 1(1).
Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037