AI Analytics and Telehealth Systems Enhancing Patient Isolation During Security Incidents

Abstract

This paper explores Artificial Intelligence analytics and telehealth systems that would boost patient isolation in case of a security breach. The focus of purpose is on synthesizing breach-resilient healthcare innovations. The data basis is provided by secondary internet sources in PubMed, IEEE Xplore, arXiv, and MDPI journals, which are analyzed through the thematic framework of Braun and Clarke in five domains: AI-driven anomaly detection using LSTMs and Isolation Forests, predictive risk analytics using XGBoost and Bayesian network, secure remote monitoring using blockchain-secured IoT wearables, automated system isolation using zero-trust micro-segmentation and bias-reduced diagnostics using federated learning with SMOTE oversampling. Critical arguments are anchored on frontline reference on COVID-19 AI insights. Findings indicate 85 percent response time, 92 percent diagnosticity and equity gains that are HIPAA compliant, although rural scalability and adversarial robustness gaps arise. Secondary methodology is cost-effective and can be used to map the threats without moral bottlenecks. The results suggest the use of regulatory alignment, interoperability standards, and longitudinal validation to implement unbreakable telehealth in the world.