Generative AI Powered Cloud Native Architecture for Intelligent Healthcare Governance and Autonomous Clinical Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2024.0706034Keywords:
Generative AI, cloud-native architecture, healthcare data governance, clinical decision intelligence, large language models, privacy preservation, explainable AI, microservices, intelligent systems, healthcare analyticsAbstract
The integration of Generative Artificial Intelligence (GenAI) into healthcare systems presents transformative opportunities for intelligent data governance and autonomous clinical decision-making. However, the increasing volume, sensitivity, and fragmentation of healthcare data pose significant challenges related to privacy, interoperability, and scalability. This paper proposes a Generative AI-powered cloud-native architecture designed to enable secure, scalable, and intelligent healthcare data governance while supporting autonomous clinical decision intelligence. The proposed framework leverages large language models and generative models to synthesize insights, automate clinical workflows, and enhance decision support systems. Cloud-native technologies such as containerization, microservices, and orchestration ensure flexibility, resilience, and efficient resource utilization. The architecture incorporates privacy-preserving mechanisms, including data anonymization, access control policies, and federated data handling, to ensure compliance with regulatory standards such as HIPAA and GDPR. Additionally, explainable AI components are integrated to improve transparency and trust in automated clinical decisions. Experimental evaluations demonstrate improvements in decision accuracy, system scalability, and operational efficiency. This research highlights the potential of combining Generative AI and cloud-native platforms to build next-generation healthcare systems that are secure, intelligent, and capable of autonomous clinical reasoning
References
1. Adepu, G. (2023). Intelligent digital government platforms: Leveraging machine learning and cloud architecture for social service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 75–92.
2. Rahman, M. W., & Hossain, M. S. (2023). Integrating Generative AI into Business Analytics for Automated Strategic Insights. Integrating Generative AI into Business Analytics for Automated Strategic Insights, 6(12), 189-219.
3. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
4. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
5. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
6. Gurram, S. (2024). The End of Generative AI Experiments Designing Production-Grade Data Architectures for LLM Systems. International Journal of Computer Technology and Electronics Communication, 7(1), 8233-8242.
7. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
8. Mali, R. K. (2023). A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8382-8391.
9. Gentyala, R. (2024). From features to financial personas: Mapping feature transformation efficacy to customer archetypes in behavioral banking data. International Journal of Computer Science and Engineering Research and Development, 14(1), 127-145.
10. Karvannan, R. (2023). Empowering healthcare operations with next-generation compliance and inventory solutions. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 297–313.
11. Bonthala, D. (2023). From Manual Controls to Autonomous Governance in Enterprise Platforms. International Journal of Research and Applied Innovations, 6(4), 9246-9253.
12. Niture, N. (2023). Machine Learning and Cryptographic Algorithms--Analysis and Design in Ransomware and Vulnerabilities Detection. Authorea Preprints.
13. Boddupally, H. L. (2023). Automating Incident Triage and Root Cause Intelligence Through Large Language Model–Driven Correlation of System Logs and Operational Metrics in Large-Scale Distributed Environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7676-7688.
14. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
15. Jagannathan, P., Gurumoorthy, S., Stateczny, A., Divakarachar, P. B., & Sengupta, J. (2021). Collision-aware routing using multi-objective seagull optimization algorithm for WSN-based IoT. Sensors, 21(24), 8496.
16. Thangaraj, S. J. J., Loganayagi, S., Vimal, V. R., Deepak, V., Banu, E. A., & Rani, J. P. A. (2023, August). Design of Internet Product Interface Based on Dynamic Model. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 92-97). IEEE.
17. Adepu, R. (2023). Zero trust architecture for large-scale enterprise infrastructure security. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 171–187.
18. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
19. Dave, B. L. (2023). Federated AI frameworks for regulated industries: Cross-domain intelligence for social services, insurance, and industrial operations. International Journal of Research and Applied Innovations, 6(1), 8346–8362.
20. Sengottaiyan, N., Gurusamy, R., Kalyanasundaram, P., Sangameswaran, B. B., Sathesh, M., & Rajasekar, M. (2023, December). Gain Improved Novel Coplanar Waveguide-Fed Sierpinski Carpet Fractal Microstrip Patch Antenna for the Acquisition of Bio-signals. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 105-109). IEEE.
21. Parupalli, “The Evolution of Financial Decision Support Systems : From BI Dashboards to Predictive Analytics,” KOS J. Bus. Manag., vol. 1, no. 1, pp. 1–8, 2023
22. Gopinathan, V. R. (2023). Cloud-first AI security architecture for protecting enterprise digital ecosystems and financial networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
23. Kunadi, S. K. (2023). Entity resolution at scale: Advanced fuzzy matching techniques for company and project data. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8014–8022.
24. Subramanyam, S. P. (2023). Secure identity and access management frameworks for cloud native DevOps systems. International Journal of Computer Technology and Electronics Communication, 6(4), 7357–7366.
25. Namdeo, A. (2023). Neuromorphic edge analytics for industrial IoT. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8113–8123.
26. Joyce, S. (2021). Beyond migration: Designing resilient SAP workloads for the next generation of cloud infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 2779–2788. https://doi.org/10.15662/IJEETR.2021.0302004
27. Parasa, M. (2023). Measuring skill graph drift in SAP SuccessFactors Talent Intelligence Hub for career mobility, workforce reskilling, and skills-based talent governance. Advanced International Journal of Multidisciplinary Research, 1(1), 1–27. https://doi.org/10.62127/aijmr.2023.v01i01.1359
28. Balamuralidhar Sarabu, V. (2023). Designing controlled data migration pipelines from on-premises to cloud platforms for mission-critical enterprise systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 13–33.
29. Mathew, A. (2023). Learning Metaverse Powered by Artificial Intelligence. Recent Progress in Science and Technology Vol. 4, 4, 134-141.
30. Lanka, S. (2023). Built for the Future How Citrix Reinvented Security Monitoring with Analytics. International Journal of Humanities and Information Technology, 5(02), 26-33.
31. Narayanan, S. (2023). Cloud-native generative artificial intelligence for autonomous third-party risk intelligence: A zero-trust supply chain assurance framework. International Journal of Computer Engineering and Technology, 14(1), 283–297. https://philarchive.org/archive/NARCGA
32. Mallireddy, S. (2023). Using ServiceNow to analyze health data in rural health authority. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 108–112.
33. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
34. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
35. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
36. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
37. Alam, M. K., Fahad, M. L. R., & Shuvo, M. S. H. (2023). Regulating the Algorithmic Bloodhound: Modernizing US Financial Regulations for the AI Era of Counter-Terrorism. Journal of Computer Science and Technology Studies, 5(2), 66-87.
38. Jagadeesh, S., & Sugumar,R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
