AI and IoT Based Intelligent Detection Systems for Healthcare Diagnostics and Smart Infrastructure

Abstract

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has led to the development of intelligent detection systems that significantly enhance healthcare diagnostics and smart infrastructure management. This paper explores the design, implementation, and impact of AI and IoT-based systems capable of real-time monitoring, data analysis, and automated decision-making. In healthcare, these systems enable early disease detection, remote patient monitoring, and personalized treatment through continuous data collection from wearable and medical devices. In smart infrastructure, IoT sensors combined with AI algorithms facilitate efficient resource management, predictive maintenance, and improved safety mechanisms. The integration of cloud computing further enhances scalability and data processing capabilities. However, the deployment of such systems introduces challenges related to data privacy, security, and system interoperability. This study presents a comprehensive framework for intelligent detection systems, highlighting architectural components, data flow mechanisms, and security strategies. The results demonstrate that AI and IoT integration improves accuracy, efficiency, and responsiveness in both healthcare and infrastructure domains. The paper concludes by emphasizing the importance of robust security frameworks and ethical considerations to ensure reliable and responsible deployment of these technologies.