Intelligent Healthcare at Scale: Data-Driven Support through Cloud Infrastructure and AI for understanding human actions

Abstract

The accelerating complexity of modern healthcare demands decision-making systems that are not only scalable but also context-aware and patient-centered. This study proposes an integrated framework that leverages behavioral artificial intelligence (AI) models deployed within a cloud-based environment to support real-time, data-driven clinical decisions. Drawing from structured electronic health records, behavioral logs, genomic datasets, and environmental metadata, the system employs a hybrid architecture combining ensemble machine learning models with reinforcement learning agents for adaptive personalization. Model performance was evaluated using precision, recall, F1-score, and latency benchmarks across multiple use cases, including thyroid cancer recurrence prediction, glioma segmentation, and behavioral adherence modeling in chronic disease management. Results demonstrate significant gains in predictive accuracy (up to 11.4% over baseline models), reduced decision latency, and improved alignment with individualized patient pathways. Additionally, the cloud-native infrastructure ensures elastic scalability, secure multi-source data ingestion, and seamless integration into existing clinical workflows. These findings highlight the transformative potential of combining behavioral AI with cloud computing to deliver proactive, high-impact, and scalable healthcare interventions. The approach sets a precedent for future clinical systems that are not only data-rich but also behaviorally intelligent and operationally resilient.