Leveraging AI-Driven Cloud Computing for Advanced Health Prediction Models

Modern healthcare systems are undergoing a paradigm shift from reactive clinical interventions to continuous, proactive risk prediction. However, deploying advanced predictive models in real-world clinical settings is severely constrained by heterogeneous data silos, computational latency, and the opaque black-box nature of complex algorithms. To overcome these challenges, this paper presents PERFORM (Patient Edge Resilient-Fast Optimized-Reliable Monitoring), a novel, multi-layered architectural framework that synergizes Artificial Intelligence (AI) with scalable hybrid Edge-Cloud computing infrastructure. The proposed framework operates across five interconnected layers: harmonizing disparate data streams (EHRs, imaging, IoMT telemetry, and multi-omics); dynamically orchestrating computational workloads between edge nodes and cloud environments to minimize processing latency; executing adaptive model selection; embedding Explainable AI (XAI) for transparent decision support; and maintaining continuous governed evolution via drift tracking and clinical feedback. By synthesizing data integration, low-latency execution, and audit-ready interpretability into an end-to-end system, this study provides a unified blueprint for deployable, safe, and clinically trustworthy risk prediction.