Leveraging Oracle Cloud and AI for Smart Healthcare: Enhancing Hospital Equipment and Data Management
DOI:
https://doi.org/10.15662/IJRAI.2025.0806811Keywords:
Oracle Cloud, Artificial Intelligence, Smart Healthcare, Predictive Maintenance, Medical Equipment Management, Clinical Data Management, Healthcare AutomationAbstract
The rapid digitalization of healthcare has intensified the need for intelligent systems that ensure the reliability of medical equipment and the efficient management of clinical data. Oracle Cloud, combined with Artificial Intelligence (AI), offers a transformative foundation for building smart healthcare ecosystems that enhance operational performance and patient safety. By integrating AI-driven predictive maintenance with Oracle’s autonomous cloud capabilities, hospitals can proactively monitor medical equipment, forecast failures, and optimize maintenance scheduling. Simultaneously, advanced data management services enable secure, scalable, and automated handling of clinical, operational, and IoT-generated data across hospital environments. This unified cloud-AI framework improves decision-making, reduces downtime, strengthens data governance, and supports continuous care delivery. The result is a more resilient, efficient, and intelligent healthcare infrastructure capable of meeting the demands of modern medical operations.
References
1. Schmidt, M. (2024). Predictive maintenance strategies for healthcare equipment using machine learning. Hong Kong Journal of AI and Medicine, 4(1), 18. hongkongscipub.com
2. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
3. Devi, Y. R., Reddy, M. J., Glory, B. K., Kumar, B. P., & Prasad, G. N. R. (2023). Optimizing hospital resource management with IoT and machine learning: A case study in predictive maintenance. Journal of Neonatal Surgery, 14(24), 5910. jneonatalsurg.com
4. Kusumba, S. (2025). Modernizing US Healthcare Financial Systems: A Unified HIGLAS Data Lakehouse for National Efficiency and Accountability. International Journal of Computing and Engineering, 7(12), 24-37.
5. Oracle. (2023). Use your data to move from reactive to predictive maintenance. Retrieved from https://www.oracle.com/apac/data-platform/predictive-maintenance/
6. Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). Balanced aware firefly optimization based cost-effective privacy preserving approach of intermediate data sets over cloud computing.
7. Mohile, A. (2022). Enhancing Cloud Access Security: An Adaptive CASB Framework for Multi-Tenant Environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7134-7141.
8. Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). Balanced aware firefly optimization based cost-effective privacy preserving approach of intermediate data sets over cloud computing.
9. Sardana, A., Kotapati, V. B. R., & Ponnoju, S. C. (2025). Autonomous Audit Agents for PCI DSS 5.0: A Reinforcement Learning Approach. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(1), 130-136.
10. SymetryML. (2023). Deploy a predictive, federated healthcare analytics platform on Oracle Cloud. Retrieved from https://docs.oracle.com/en/solutions/symetryml-on-oci/index.html
11. Karanjkar, R. (2022). Resiliency Testing in Cloud Infrastructure for Distributed Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7142-7144.
12. Kesavan, E. (2025). Salesforce Classic as Well as Lightning Automation using Tosca Automation and Tosca AI-Powered Salesforce Engine. i-Manager's Journal on Information Technology, 14(2). https://www.proquest.com/openview/eb4a630e1b01b6227e56cab16e747ccc/1?pq-origsite=gscholar&cbl=2030619
13. Peram, S. R. (2025). Machine Learning-Based performance evaluation and memory usage forecasting for intelligent systems. Journal of Artificial Intelligence and Machine Learning, 3(3), 275. https://www.researchgate.net/profile/Sudhakara-Peram/publication/395586137_Machine_Learning-Based_Performance_Evaluation_and_Memory_Usage_Forecasting_for_Intelligent_Systems/links/68cbbd13d221a404b2a0abbf/Machine-Learning-Based-Performance-Evaluation-and-Memory-Usage-Forecasting-for-Intelligent-Systems.pdf
14. Kandula, N. Innovative Fabrication of Advanced Robots Using The Waspas Method A New Era In Robotics Engineering. IJRMLT 2025, 1, 1–13. [Google Scholar] [CrossRef]
15. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
16. Sourav, M. S. A., Asha, N. B., & Reza, J. (2025). Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth. Journal of Computer Science and Technology Studies, 7(11), 224-247.
17. Uddandarao, D. P. Improving Employment Survey Estimates in Data-ScarceRegions Using Dynamic Bayesian Hierarchical Models: Addressing Measurement Challenges in Developing Countries. Panamerican Mathematical Journal, 34(4), 2024. https://doi.org/10.52783/pmj.v34.i4.5584
18. Christadoss, J., & Panda, M. R. (2025). Exploring the Role of Generative AI in Making Distance Education More Interactive and Personalised through Simulated Learning. Futurity Proceedings, (4), 114-127.
19. Dendukuri, S. V. (2025). Federated Learning in Healthcare: Protecting Patient Privacy While Advancing Analytics. Journal of Computer Science and Technology Studies, 7(7), 840-845.
20. Joseph, J. (2023). Trust, but Verify: Audit-ready logging for clinical AI. https://www.researchgate.net/profile/JimmyJoseph9/publication/395305525_Trust_but_Verify_Audit -ready_logging_for_clinical_AI/links/68bbc5046f87c42f3b9011db/Trust-but-Verify-Audit-readylogging-for-clinical-AI.pdf
21. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
22. Raj, A. A., & Sugumar, R. (2023, June). Early Detection of COVID-19 with Impact on Cardiovascular Complications using CNN Utilising Pre-Processed Chest X-Ray Images. In 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC) (pp. 1-6). IEEE.
23. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
24. Rahman, M., Arif, M. H., Alim, M. A., Rahman, M. R., & Hossen, M. S. (2021). Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis. https://www.researchgate.net/profile/Md-Abdul-Alim-18/publication/395920517_Quantum_Machine_Learning_Integration_A_Novel_Approach_to_Business_and_Economic_Data_Analysis/links/68d8103802d6215259b67085/Quantum-Machine-Learning-Integration-A-Novel-Approach-to-Business-and-Economic-Data-Analysis.pdf
25. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
26. Hennebelle, A., Materwala, H., & Ismail, L. (2023). HealthEdge: A machine learning-based smart healthcare framework for prediction of type 2 diabetes in an integrated IoT, edge, and cloud computing system. arXiv preprint arXiv:2301.10450. arXiv





