Abstract
The rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) has significantly transformed modern healthcare systems, particularly through the integration of Edge AI for smart healthcare applications. IoT-enabled medical devices continuously collect large volumes of patient data, including vital signs, physiological parameters, and environmental conditions. However, traditional cloud-based processing often faces challenges such as latency, bandwidth dependency, privacy risks, and delayed clinical responses. Edge AI addresses these limitations by enabling real-time data processing and intelligent decision-making closer to the data source, thereby enhancing system responsiveness, reliability, and security. This research explores the role of IoT combined with Edge AI in developing smart healthcare ecosystems that support real-time monitoring, early disease detection, personalized treatment, and efficient clinical workflows. The study highlights the architectural framework of IoT–Edge AI systems, their practical applications in remote patient monitoring, medical imaging, and emergency healthcare, as well as their potential to reduce operational costs and improve patient outcomes. Furthermore, the paper discusses key challenges, including data security, interoperability, computational limitations, and ethical considerations related to patient privacy and algorithmic transparency.
The findings indicate that IoT integrated with Edge AI significantly enhances healthcare efficiency by enabling faster decision-making, reducing network dependency, and improving data privacy. As healthcare systems continue to evolve, the convergence of IoT and Edge AI is expected to play a crucial role in enabling predictive, preventive, and personalized healthcare services, contributing to sustainable and intelligent healthcare infrastructures worldwide.