A 6G-Enabled AI-Powered Intelligent Healthcare Integrating IoMT and Edge Computing

Abstract

Advancements in wireless communication technologies are reshaping healthcare by facilitating intelligent, real-time, and patient-centered services. Current 5G-based healthcare infrastructures face challenges related to latency, reliability, scalability, and data processing, limiting their support for applications such as remote robotic surgery, immersive telemedicine, and continuous critical patient monitoring. To overcome these issues, this study introduces a 6G-enabled Intelligent Healthcare System (6G-IHS) that combines the Internet of Medical Things (IoMT), edge intelligence, 6G communication, and AI-driven cloud computing within a multi-layered architecture. The framework targets stringent 6G requirements, including sub-millisecond latency, ultra-high data rates, and ultra-reliable low-latency communication (URLLC). A mathematical model characterizes key parameters such as end-to-end latency, data rate, reliability, energy consumption, and AI diagnostic performance. An edge processing mechanism is incorporated to lower latency and support real-time decisions, while AI enables predictive analytics and intelligent healthcare functions. Simulation results indicate that 6G-IHS surpasses traditional cloud-based and 5G systems, achieving latency under 1 ms, data rates above 100 Gbps, reliability over 99.9999%, and up to 30% energy reduction in IoMT devices. AI integration enhances diagnostic accuracy to 90–98%, supporting early detection and personalized treatment. This framework offers a scalable, secure, and efficient approach for future healthcare systems, illustrating the potential of 6G to advance intelligent, connected healthcare environments.