Edge AI and Embedded Systems in the Internet of Medical Things: Architectures, Applications, and Challenges
Year: 2026
Type: conference-paper
Abstract: This study reviews Edge Artificial Intelligence (Edge AI)-empowered embedded systems for the Internet of Medical Things (IoMT). Their architecture, application areas, and key challenges are explored herein. The evolution of the IoT has revolutionized the area of health and medicine because of continuous monitoring and intelligent decision making from the collected information. Yet, traditional IoT implementations have shown some problems like latency, bandwidth issues, and security problems. Therefore, a viable solution has been developed where embedded systems powered by Edge AI have become increasingly popular. The review addresses the layered architecture of the IoMT involving medical sensors, embedded devices, communications, and clouds. Key applications in the area include real-time patient monitoring, wearable health devices, remote diagnostics, and ICU. Key challenges of embedding Edge AI include constrained resources, power management, security issues, and interoperability. Future trends in Edge AI research and development may address lightweight models, federated learning, explainable AI, and hybrid edge-cloud approaches. Overall, the paper highlights how Edge AI-embedded systems can be used for healthcare purposes.
Institution: Université Ibn-Tofail
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204111995