Integrated Cloud-AI and Oracle Machine Learning Model for Secure Data Analytics and Testing in Healthcare and Financial Services
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806804Keywords:
Clinical Decision Support System, Oracle Cloud Infrastructure, Artificial Intelligence, Neurosymbolic AI, Healthcare Informatics, Model Explainability, Workflow Efficiency, Patient SafetyAbstract
AI‑assisted clinical decision support systems (CDSS) are transforming healthcare by enabling more accurate, efficient, and personalized care. Oracle Cloud Infrastructure (OCI) offers cloud compute, storage, AI services, and robust security which can underpin the deployment of scalable CDSS. This paper surveys the recent developments in AI‐assisted clinical decision support delivered via OCI, discusses methodology of implementation, advantages and disadvantages, reports early results, and suggests directions for future work. Two recent case studies are examined: Oracle’s Clinical AI Agent which reduces physician documentation time significantly across many specialties; and Evidium’s neurosymbolic AI platform hosted on OCI, which transforms unstructured clinical data into structured knowledge, enhancing predictive modeling and decision support. Methodological challenges include data privacy, interoperability, clinician acceptance, model explainability, and cost. Advantages found include reduced administrative burden, improved efficiency, scalability, strong security, faster model training, and enhanced clinician focus on patient care. Disadvantages include risk of bias, dependency on cloud infrastructure, regulatory compliance, expensive implementation, potential workflow disruption, and need for clinician trust and transparency. Results from Oracle’s AI Agent show ~30% reduction in documentation time; from Evidium, improved performance in model training time and data processing. Discussion analyzes how these improvements translate into patient outcomes, clinician satisfaction, and cost savings, as well as the trade‑offs. Conclusion: deploying AI‑assisted decision support on OCI holds strong promise for smart healthcare delivery but requires careful attention to governance, transparency, and human‑AI collaboration. Future work should include rigorous clinical trials, measurement of patient health outcome impact, cost‐benefit analyses, extending to low‑resource settings, and improving model interpretability and bias mitigation.
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