Quantum ML DevOps Architecture for Serverless Healthcare: Ethical AI and Rule Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506014Keywords:
DevOps, serverless cloud, healthcare IT, quantum machine learning, business rule intelligence, continuous delivery, ethical AI, healthcare analyticsAbstract
In today’s healthcare landscape, cloud‑native architectures, serverless computing, continuous delivery (DevOps) workflows, business‑rule intelligence, and emerging quantum machine learning (QML) methods converge to offer novel capabilities—but also present significant complexity and risks. This paper proposes a unified DevOps‑centric architecture that integrates serverless cloud infrastructure for healthcare, hybrid quantum‑classical machine learning models for advanced analytics, business‑rule automation for intelligent workflow decisions, and ethical‑AI governance baked into the delivery pipeline. The architecture supports continuous integration/continuous delivery (CI/CD) of healthcare analytics and services, enabling rapid deployment, scalable inference, rule‑based decisioning, and ethical compliance by design. We present how the pipeline ingests health‑data assets (EHR, streaming device/IoT data), processes them via hybrid quantum‑classical models, applies decision‑logic through business‑rule engines, and releases updates via a DevOps workflow with audit, governance and traceability. A simulation study demonstrates improved analytic throughput and deployment agility, while highlighting latency, integration, and governance trade‑offs. The results reveal potential gains in delivering advanced analytics and rule‑driven decision support, yet also underscore limitations in quantum readiness, complexity of rule integration, and ethical audit challenges. We conclude with recommendations for implementing such architectures, and outline future research directions focusing on real‑world evaluation, ethical‑by‑design automation, and bridging quantum‑analytics into regulated healthcare DevOps
References
1. Badmus, A., Adebayo, M.E., Ehigie, D.E. (2018). Secure and scalable model lifecycle management in healthcare AI: A DevOps approach for privacy, compliance, and traceability. Scholars Journal of Medical Case Reports, 6(12), 1087 1099.
2. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
3. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
4. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.
5. Nelson, M.L., Sen, R. (2014). Business rules management in healthcare: A lifecycle approach. Decision Support Systems, 57(1), 387 394.
6. Sudarsan, V., & Sugumar, R. (2018). Calculate Euclidean Distance for Distributed Environment Using Weka Code. International Journal of Pure and Applied Mathematics, 118(9), 951-957.
7. Anugula Sethupathy, Utham Kumar. (2019). Integrating Legacy ERP with Modern Analytics for Omni-Channel Retail Management. Journal of Emerging Technologies and Innovative Research. 6. 357-368. 10.56975/jetir.v6i9.568594.
8. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111.
9. Peddamukkula, P. K. Ethical Considerations in AI and Automation Integration Within the Life Insurance Industry. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017494_Ethical_Considerations_in_AI_and_Automation_Integration_Within_the_Life_Insurance_Industry/links/690239c04baee165918ee584/Ethical-Considerations-in-AI-and-Automation-Integration-Within-the-Life-Insurance-Industry.pdf
10. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.
11. KM, Z., Akhtaruzzaman, K., & Tanvir Rahman, A. (2022). BUILDING TRUST IN AUTONOMOUS CYBER DECISION INFRASTRUCTURE THROUGH EXPLAINABLE AI. International Journal of Economy and Innovation, 29, 405-428.
12. Chaware, S., Karandikar, A. (2014). A review of methods for developing business rules engine. International Journal of Advanced Research in Computer and Communication Engineering, 3(3).
13. Sardana, A., Kotapati, V. B. R., & Shanmugam, L. (2020). AI-Guided Modernization Playbooks for Legacy Mission-Critical Payment Platforms. Journal of Artificial Intelligence & Machine Learning Studies, 4, 1-38.
14. Cherukuri, B. R. (2020). Quantum machine learning: Transforming cloud-based AI solutions. https://www.researchgate.net/profile/Bangar-Raju-Cherukuri/publication/388617417_Quantum_machine_learning_Transforming_cloud-based_AI_solutions/links/67a33efb645ef274a46db8cf/Quantum-machine-learning-Transforming-cloud-based-AI-solutions.pdf
15. “Quantum Algorithms for Cognitive Healthcare.” (2018). UCL Quantum Science & Technology Institute Report.
16. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7649
17. “Quantum machine learning: Transforming cloud based AI solutions.” (2020). IJSRA.
18. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
19. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
20. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
21. Sasidevi Jayaraman, Sugumar Rajendran and Shanmuga Priya P., “Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud,” Int. J. Business Intelligence and Data Mining, Vol. 15, No. 3, 2019.
22. “AI Driven DevOps Practices for Healthcare Data Security and Compliance.” (2021). International Journal of Intelligent Systems and Applications in Engineering.
