AI at the Edge: Enhancing Cybersecurity and Real-Time Decision-Making in Healthcare IoT Systems

Abstract

The integration of Artificial Intelligence (AI) with edge computing and Internet of Things (IoT) systems has revolutionized healthcare, enabling real-time decision-making and personalized care delivery. However, these advancements also introduce significant cybersecurity challenges that threaten patient privacy and system integrity. This research explores the convergence of AI, edge computing, and IoT in healthcare, focusing on enhancing real-time decision-making while mitigating cybersecurity risks. By leveraging edge computing’s decentralized architecture, this study aims to reduce latency, optimize resource utilization, and ensure data security during transmission and processing. Machine learning models at the edge facilitate real-time analytics and decision-making, while robust cybersecurity frameworks, including encryption, anomaly detection, and secure communication protocols, are proposed to safeguard sensitive healthcare data. The research also examines the ethical implications and scalability of deploying secure AI-driven IoT systems in healthcare. By addressing these challenges, the study aims to create a resilient infrastructure for delivering efficient, secure, and reliable healthcare solutions.