Next-Generation Intelligent Clinical Computing Using Explainable AI and Modern Cloud Infrastructure
DOI:
https://doi.org/10.15680/e13d3g18Keywords:
Explainable Artificial Intelligence, Intelligent Clinical Computing, Cloud Infrastructure, Healthcare Analytics, Medical Decision Support, Clinical Intelligence, Machine Learning, Predictive Healthcare, Cloud Computing, Explainable Healthcare Systems, Electronic Health Records, Telemedicine, Medical Imaging Analytics, Adaptive Healthcare Systems, Intelligent Medical EcosystemsAbstract
The rapid advancement of healthcare technologies, artificial intelligence, and cloud computing has transformed clinical computing systems into highly intelligent, data-driven environments capable of supporting advanced medical decision-making and healthcare management. Modern healthcare institutions increasingly rely on intelligent clinical computing frameworks for disease diagnosis, predictive analytics, patient monitoring, medical imaging, and personalized treatment planning. However, many artificial intelligence models used in healthcare operate as black-box systems, limiting transparency, interpretability, and trust among healthcare professionals and patients. Explainable Artificial Intelligence (XAI) has emerged as a promising solution for enhancing transparency and accountability in intelligent clinical systems by enabling clinicians to understand the reasoning behind automated predictions and recommendations. Simultaneously, modern cloud infrastructure provides scalable, flexible, and cost-efficient computational resources for storing and processing large volumes of healthcare data generated from electronic health records, wearable devices, telemedicine systems, and Internet of Medical Things devices. This study explores next-generation intelligent clinical computing frameworks that integrate explainable AI with modern cloud infrastructure to improve healthcare efficiency, security, scalability, and clinical decision support. The research examines architectural models, explainability techniques, cloud-based analytics, implementation methodologies, benefits, and limitations associated with intelligent healthcare ecosystems. The study highlights the role of explainable and cloud-enabled clinical computing in supporting resilient, transparent, and adaptive healthcare systems for future digital medical environments.
References
1. Grandhe, K. (2025, December). AI Powered Fraud Detection in SAP S/4HANA Finance. In 2025 1st International Conference on Data Science and Intelligent Network Computing (ICDSINC) (pp. 468-472). IEEE.
2. Soundappan, S. J. (2025). Privacy Preserving Data Analytics Frameworks using Homomorphic Encryption Techniques. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14531.
3. Vayyasi, N. K. (2023). Retail fraud analytics using generative intelligence and Java cloud frameworks. International Journal of Science, Research and Technology (IJSRAT), 6(4), 10324–10337.
4. Bonthala, D. (2025). Telemetry Driven Cost Governance for Enterprise Data and AI Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9361-9372.
5. Ratkunas, V., Misiulis, E., Lapinskiene, I., Skarbalius, G., Navakas, R., Dziugys, A., ... & Petkus, V. (2024). Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study. European Journal of Radiology, 176, 111483.
6. Mallireddy, S. (2021). Data encryption and policies via digital transformations and services. International Journal of Research and Applied Innovations, 4(5), 1–6.
7. Sengupta, J., Alzbutas, R., Iešmantas, T., Petkus, V., Barkauskienė, A., Ratkūnas, V., ... & Džiugys, A. (2024). Detection of Subarachnoid Hemorrhage Using CNN with Dynamic Factor and Wandering Strategy-Based Feature Selection. Diagnostics, 14(21), 2417.
8. Sharma, K. P., Kumar, I., Singh, P. P., Anbazhagan, K., Albarakati, H. M., Bhatt, M. W., ... & Rana, A. (2024). Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles. Computers in Human Behavior, 153, 108110.
9. Bellundagi, M. (2023). Blockchain-Based Secure Data Sharing Framework for Smart Applications. International Journal of Future Innovative Science and Technology (IJFIST), 6(2), 10268.
10. Hossain, M. S., Ali, M., & HOSSAIN, M. S. (2023). AI-Enhanced Labor Market Analytics to Predict Workforce Shifts and Support Policy Decisions in the US Economy. Journal of Computer Science and Technology Studies, 5(1), 101-120.
11. Tiwari, S. K. (2025). Automating Behavior-Driven Development with Generative AI: Enhancing Efficiency in Test Automation. Frontiers in Emerging Computer Science and Information Technology, 2(12), 01-14.
12. Kunadi, S. K. (2023). Integrating third-party data (D&B, ZoomInfo, construction feeds) into a unified data model. International Journal of Science, Research and Technology, 6(5), 10661–10671.
13. Sarabu, V. B. (2026). Enterprise reconciliation architectures for financially critical platform transitions: A framework for accuracy and control during system replacement. International Journal of Research and Applied Innovations (IJRAI), 9(2), 9–31.
14. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.
15. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
16. Adepu, G. (2024). AI-driven healthcare payment systems using intelligent claims validation and fraud detection mechanisms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 259–277.
17. Appani, C. (2024). Explainable AI for fraud detection in financial transactions. Journal of Information Systems Engineering and Management, 9(3). https://jisem-journal.com/download/32_Explainable_AI_for_Fraud_Detection.pdf
18. 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.
19. Mudusu, S. K. (2025). AI-driven data engineering in the Internet of Things: Scaling data pipelines for smart device ecosystems. ISCSITR-International Journal of Data Engineering (ISCSITR-IJDE), 6(1), 1–9.
20. Karvannan, R. (2024). Human AI partnerships: Unlocking a more efficient, healthier future. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11243–11255.
21. Parupalli, A., & Pandya, S. (2022). Compliance-Driven Data Governance: A Survey on GDPR, and HIPAA in Cloud Databases. vol, 12, 828-836.
22. Adepu, R. (2026). Autonomous cyber defense systems powered by AI for enterprise cloud environments. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(2), 23–41.
23. Patel, M., & Chaturvedi, V. (2025). A survey on artificial intelligence techniques for disease prediction in healthcare. ESP Journal of Engineering & Technology Advancements, 5(4), 201–210.
24. Parupalli, A. (2023). The Evolution of Financial Decision Support Systems: From BI Dashboards to Predictive Analytics. KOS J. Bus. Manag, 1(1), 1-8.
25. Prasad, P. K. (2025). Federated Agentic SRE—Cross-Vendor, PrivacyPreserving Agent Federations for Hybrid Multi-Cloud Incident Response. Journal of Computational Analysis & Applications, 34(11).
26. Boddupally, H. L. (2025). Next-Generation Code Transformation for Legacy. NET Systems with Generative AI. Available at SSRN 6270698.
27. Kasireddy, J. R. (2025). Leveraging big data analytics for enhanced commercial vehicle safety: FMCSA's data engineering journey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 3203–3222. https://doi.org/10.32628/CSEIT25112796
28. 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.
29. Kunadi, S. K. (2022). Building scalable master data management systems for enterprise data platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(2), 4830–4843.
30. Dave, B. L. (2024). Harnessing Artificial Intelligence for Salesforce Metadata Advanced Migration Strategies and Strategic Business Benefits. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11398-11408.
31. Nallamothu, T. K. (2023). Generative AI in healthcare: Automating clinical documentation, diagnostics, and knowledge synthesis. International Journal of Computer Technology and Electronics Communication, 6(1), 6376–6392.
32. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
33. Panda, S. S. (2024). Delivering Scalable Cloud Services in China: Microsoft and 21Vianet Collaboration. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11325-11333.
34. Mathew, A., Jackson, E., & Tobesman, A. (2025). Agentic AI: A Game-Changer in Cybersecurity Defense. Science and Technology: Developments and Applications Vol. 7, 112-120.
35. Yamsani, N. (2016). Designing enterprise-wide reference data foundations for consistency, control, and operational integrity across complex institutional environments. International Journal of Scientific Research & Engineering Trends, 2(5). https://doi.org/10.5281/zenodo.18296676
36. Lanka, S. (2022). Building smarter security systems with AI: Inside Citrix analytics for security. Journal of Advanced Research Engineering and Technology (JARET), 1(2), 93–109. https://doi.org/10.34218/JARET_01_02_009
37. Suddala, V. R. A. K. (2025). Healthcare e-commerce platforms driving secure, scalable, and auditable service delivery. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9340–9351.
38. Imtiaz, N., Kundu, T. R., Roy, A., Bhuiyan, M. I. H., Rahman, K., & Islam, M. K. (2025). Governance Readiness Beyond Predictive Performance: An Empirical Benchmark for Higher-Education Early Warning Systems. Frontiers in Computer Science and Artificial Intelligence, 4(5), 49-65.
39. Gentyala, R. (2025). Bridging the semantic divide: A framework for cross-functional literacy between data and machine learning engineers. European Journal of Advances in Engineering and Technology, 12(4), 91–100.
40. Gopinathan, V. R. (2025). Design and Implementation of Scalable Distributed Machine Learning in Multi-Cloud Infrastructures. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17211.
41. Bonthala, D. (2026). Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 641-650.
42. 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.
