AI-Orchestrated Communication in Software-Defined Networks: Enhancing Medical Imaging Efficiency and Risk Intelligence via Oracle Cloud Integration

Carmen Teresa Fernández Gómez

doi:10.15662/IJRAI.2024.0705012

Authors

Carmen Teresa Fernández Gómez Data Engineer, Spain Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0705012

Keywords:

Artificial Intelligence (AI), Software-Defined Networking (SDN), Oracle Cloud Infrastructure (OCI), Medical Imaging, Risk Intelligence, Machine Learning (ML), Deep Reinforcement Learning (DRL), Network Orchestration, Cloud-Edge Integration, Predictive Analytics, Healthcare Informatics, Real-Time Communication

Abstract

The convergence of Artificial Intelligence (AI) and Software-Defined Networking (SDN) is reshaping the landscape of intelligent, adaptive, and secure network architectures. This paper introduces an AI-orchestrated communication framework that integrates SDN with Oracle Cloud Infrastructure (OCI) to optimize performance in medical imaging and risk intelligence applications. The proposed system leverages machine learning (ML) and deep reinforcement learning (DRL) models to dynamically orchestrate network resources, minimize latency, and ensure efficient data flow between cloud, edge, and diagnostic systems. By harnessing OCI’s scalable compute and GPU-accelerated environments, the framework enhances medical image processing speeds, improves diagnostic accuracy, and facilitates real-time analytics for clinical decision-making. In parallel, AI-driven risk intelligence modules employ predictive analytics to detect anomalies, forecast failures, and fortify data security across the network. Experimental evaluations demonstrate up to a 40% improvement in communication efficiency and a 30% reduction in processing delay compared to conventional SDN implementations. This research underscores the transformative potential of AI-enabled SDN orchestration in realizing intelligent, reliable, and high-performance infrastructures for healthcare and risk management domains.

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