Semantic Driven Multi Agent Systems for Real Time Healthcare Analytics Using AI within Cloud Environments
Abstract
The integration of Artificial Intelligence (AI), semantic technologies, and multi-agent systems (MAS) within cloud environments has revolutionized real-time healthcare analytics. This paper presents a semantic-driven multi-agent architecture designed to enhance healthcare data processing, interoperability, and decision-making. The proposed system leverages ontologies and semantic reasoning to enable intelligent agents to interpret heterogeneous medical data, ensuring context-aware analytics across distributed cloud platforms. By incorporating AI techniques such as machine learning and deep learning, the system facilitates predictive diagnostics, patient monitoring, and clinical decision support in real time. Cloud infrastructure ensures scalability, elasticity, and high availability, making it suitable for large-scale healthcare ecosystems. The study addresses challenges such as data heterogeneity, latency, security, and interoperability by integrating semantic frameworks with agent-based coordination mechanisms. Experimental evaluations demonstrate improved accuracy, reduced response time, and enhanced system adaptability compared to traditional healthcare analytics systems. The proposed framework supports dynamic resource allocation and intelligent collaboration among agents, enabling efficient processing of streaming healthcare data. This research contributes to advancing smart healthcare systems by combining semantic intelligence, distributed AI agents, and cloud computing to deliver robust, scalable, and real-time healthcare analytics solutions.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
15329-15338 |
Published |
November 19, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Julio Cortes (%2025). Semantic Driven Multi Agent Systems for Real Time Healthcare Analytics Using AI within Cloud Environments. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15329-15338. https://doi.org/10.15662/IJSRAT.2025.0806006 |
References
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40. 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.
2. Kumar, L. M. S. (2025). Security Across Services in Microservice Architecture. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 15(3), 89-101.
3. 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).
4. Padala, S. (2021). Cloud-Enabled AI Contact Centers in Oncology Care. International Journal of AI, BigData, Computational and Management Studies, 2(3), 93-98.
5. Nair, S. G. (2025). Cloud Reliability Engineering for Design Collaboration Platforms: Building 99.99% Availability with Multi-Region Failover. International Journal of Communication Networks and Information Security, 17(8), 66-72.
6. Alam, M. K., Mahmud, M. A., & ALAM, M. A. (2025). Adversarial Machine Learning for Robust Fraud Detection in High-Frequency Financial Transactions. Journal of Computer Science and Technology Studies, 7(8), 314-335.
7. Rahman, M. B., Ahmad, S., Kanojiya, S., Yasin, M., & Hasan, M. (2025). Cost-Effective Healthcare Operations: Financial Modeling and Optimization Using Business Intelligence Tools. Nvpubhouse Library for International Journal of Medical Science and Public Health Research, 6(10), 80-106.
8. Konda, S. K. (2025). A smart energy consumption system architecture for sustainable semiconductor manufacturing and AI workload operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(2), 9678–9694. https://doi.org/10.15662/IJEETR.2025.070200
9. Sanepalli, Uttama Reddy. (2023). Cognitive goal-driven financial infrastructure: A cloud-native, AI-orchestrated architecture for investment trade settlement and risk management systems. World Journal of Advanced Research and Reviews, 19(1), 1659–1667. https://doi.org/10.30574/wjarr.2023.19.1.1358
10. Ireddy, R. K. (2024). Event-native financial onboarding platforms: A Kafka-centric reference architecture for sub-minute identity and compliance processing. World Journal of Advanced Research and Reviews, 21(2), 2182–2192. https://doi.org/10.30574/wjarr.2024.21.2.0448
11. 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.
12. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
13. Md, S., Md Saiful, I., Mohammad, Y., Mahzabin Binte, R., & Jannatul, F. (2024). AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization. AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization, 7(12), 1830-1856.
14. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
15. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.
16. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880-885). IEEE.
17. Gupta, M., Sowmiya, S., Parmar, Y., Menon, S. V., Banchhor, C. O., & Vigenesh, M. (2024, November). Refining Heart Disease Diagnosis with Machine Learning: Techniques for Optimal Medical Outcomes. In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-5). IEEE.
18. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
19. Niture, N. (2025). AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11802-11814.
20. Kothokatta, L. (2025). Security-Integrated Test Framework for FedRAMP-Ready Cloud Applications. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(2), 9705-9714.
21. Gurram, S. (2023). Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9028-9036.
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. Bheemisetty, N. (2024). AI-powered recommendation systems: Best practices and real-world applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13928–13926. https://doi.org/10.15662/IJFIST.2024.0706011
24. Ambalakannu, M. (2024). The emergence of AI-powered data analytics revolutionizing business intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13947–13955. https://doi.org/10.15662/IJFIST.2024.0706014
25. Indurthy, V. S. K. (2024). The surge in AI-powered data analytics revolutionizing business intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13956–13964. https://doi.org/10.15662/IJFIST.2024.0706015
26. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
27. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342-5351.
28. Mangukiya, M. (2023). Blockchain-Enabled Traceability and Compliance in Global Electronics Production Networks. International Journal of Computer Technology and Electronics Communication, 6(6), 7999-8004.
29. Khan, M. F., Khan, W. A., Hameed, M. M., & Siddiqi, A. A. (2025). Self-Awareness Mechanism for Top-down Attention using Fuzzy Logic in Sustainable Business Intelligence. Sustainable Business and Society in Emerging Economies, 7(2), 241-250.
30. Guda, D. P. (2024). Cyber insurance for DevSecOps risks: Pricing models and coverage gaps. Journal of Information Systems Engineering and Management, 9(3).
31. Md, S., Md Saiful, I., Mohammad, Y., Mahzabin Binte, R., & Jannatul, F. (2024). AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization. AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization, 7(12), 1830-1856.
32. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
33. Subramani, V. (2025). Enterprise Cloud Evolution: Enhancing Performance, Reliability and Cost Efficiency. ISCSITR-INTERNATIONAL JOURNAL OF CLOUD COMPUTING (ISCSITR-IJCC)-ISSN (Online): 3067-7378, 6(5), 6-22.
34. 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.
35. Agarwal, S. (2025). AI-Driven Incident Management in Microservices: A Scalable and Cost-Effective Framework for Proactive Site Reliability. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (ISCSITR-IJCSE)-ISSN: 3067-7394, 6(4), 15-28.
36. Gentyala, R. (2021). The Silent Interruption: Assessing the Impact of an AI Driven Sepsis Alert on Emergency Clinician Cognitive Load and Point-of-Care Efficiency. IACSE - International Journal of Computer Technology (IACSE-IJAIA), 2(1), 7–79.
37. 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.
38. Karni, S. (2025). Serverless & Event-Driven Architectures: Redefining Distributed System Design. Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 7(10), 13-22.
39. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
40. 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.