Multimodal AI–Driven Real-Time Decision Intelligence for Secure Healthcare Cloud and SAP Data Networks
DOI:
https://doi.org/10.15662/IJEETR.2025.0704007Keywords:
Multimodal AI, Real-Time Decision Intelligence, SAP Healthcare Systems, Secure Cloud Computing, Healthcare Data Analytics, Network Security, Big DataAbstract
The convergence of healthcare digitization, SAP enterprise systems, and cloud-native infrastructures has generated large volumes of heterogeneous data that demand timely, secure, and intelligent decision-making. Multimodal AI, capable of integrating structured SAP data, unstructured clinical records, medical images, network telemetry, and real-time streams, offers a powerful foundation for advanced decision intelligence. This paper presents a Multimodal AI–Driven Real-Time Decision Intelligence framework designed for secure healthcare cloud and SAP data networks. The proposed architecture unifies data ingestion, feature fusion, and analytics across cloud platforms while enforcing strong security controls, data governance, and risk-aware access policies. AI models leverage multimodal learning to deliver predictive insights, anomaly detection, and operational recommendations in near real time. The framework supports interoperability across healthcare applications, SAP business processes, and networked data ecosystems, enabling scalable analytics without compromising privacy or compliance. By combining multimodal intelligence with cloud security and SAP integration, the solution enhances clinical, operational, and strategic decision-making in complex healthcare environments.
References
1. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
3. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
4. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113. https://doi.org/10.1145/1327452.1327492
5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
6. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.
7. 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.
8. Rahanuma, T., Sakhawat Hussain, T., Md Manarat Uddin, M., & Md Ashiqul, I. (2024). Healthcare Investment Trends: A Post-COVID Capital Market Analysis Investigating How Public Health Crises Reshape Healthcare Venture Capital and M&A Activity. American Journal of Technology Advancement, 1(1), 51-79.
9. Navandar, P. Mitigating Financial Fraud in Retail through ERP System Controls: A Comprehensive Approach with SAP Solutions. https://www.researchgate.net/profile/Pavan-Navandar/publication/385076556_Mitigating_Financial_Fraud_in_Retail_through_ERP_System_Controls_A_Comprehensive_Approach_with_SAP_Solutions/links/675a0cae72215358fe28793d/Mitigating-Financial-Fraud-in-Retail-through-ERP-System-Controls-A-Comprehensive-Approach-with-SAP-Solutions.pdf
10. Akter Tohfa, N., Alim, M. A., Arif, M. H., Rahman, M. R., Rahman, M., Rasul, I., & Hossen, M. S. (2025). Machine learning–enabled anomaly detection for environmental risk management in banking. World Journal of Advanced Research and Reviews, 28(3), 1674–1682. https://doi.org/10.30574/wjarr.2025.28.3.4259
11. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
12. Sugumar, R. (2024). Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
13. Bussu, V. R. R. (2024). End-to-End Architecture and Implementation of a Unified Lakehouse Platform for Multi-ERP Data Integration using Azure Data Lake and the Databricks Lakehouse Governance Framework. International Journal of Computer Technology and Electronics Communication, 7(4), 9128-9136.
14. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.
15. Thambireddy, S. (2021). Enhancing Warehouse Productivity through SAP Integration with Multi-Model RF Guns. International Journal of Computer Technology and Electronics Communication, 4(6), 4297-4303.
16. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.Paul, D. et al., "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach," Journal of Science & Technology, vol. 2, no. 3, Sept. 8, (2021). https://thesciencebrigade.com/jst/article/view/382
17. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
18. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
19. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.
20. Mahajan, N. (2023). A predictive framework for adaptive resources allocation and risk-adjusted performance in engineering programs. Int. J. Intell. Syst. Appl. Eng, 11(11s), 866.
21. Chivukula, V. (2020). Use of multiparty computation for measurement of ad performance without exchange of personally identifiable information (PII). International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1546–1551.’
22. Ramakrishna, S. (2024). Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(Special Issue 1), 1-7.
23. Lokeshkumar Madabathula, “AI- Driven Risk Management in Finance: Predictive Models for Market Volatility, International Journal of Information Technology and Management Information Systems 16 ( 2 ): 293–302.
24. Meka, S. (2025). Redefining Data Access: A Decentralized SDK for Unified and Secure Data Retrieval. Journal Code, 1325, 7624.
25. Natta P K. AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights[J]. Journal of Computer Science and Technology Studies, 2025, 7(2): 146-152.
26. Karnam, A. (2021). The Architecture of Reliability: SAP Landscape Strategy, System Refreshes, and Cross-Platform Integrations. International Journal of Research and Applied Innovations, 4(5), 5833–5844. https://doi.org/10.15662/IJRAI.2021.0405005
27. Singh, A. Interference Testing in Dense Urban Environments: A Research Paper. environments, 6, 7. https://www.researchgate.net/profile/Abhishek-Singh-679/publication/393804878_Volume_12_Issue_2_Interference_Testing_in_Dense_Urban_Environments_A_Research_Paper/links/687bedbd1a77b36b5b0427ab/Volume-12-Issue-2-Interference-Testing-in-Dense-Urban-Environments-A-Research-Paper.pdf
28. Kabade, S., Sharma, A., & Kagalkar, A. (2024). Securing Pension Systems with AI-Driven Risk Analytics and Cloud-Native Machine Learning Architectures. International Journal of Emerging Research in Engineering and Technology, 5(2), 52-64.
29. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
30. Kasireddy, J. R. (2023). Optimizing multi-TB market data workloads: Advanced partitioning and skew mitigation strategies for Hive and Spark on EMR. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6982–6990. https://doi.org/10.15680/IJCTECE.2023.0603005
31. Madathala, H., Thumala, S. R., Barmavat, B., & Prakash, K. K. S. (2024). Functional consideration in cloud migration. International Peer Reviewed/Refereed Multidisciplinary Journal (EIPRMJ), 13(2).
32. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
33. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
