AI-Assisted and AoI-Aware Transmission Optimization for Immersive Healthcare Networks

Metaverse-assisted healthcare and immersive telemedicine rely on timely delivery of physiological sensor data to maintain accurate patient monitoring and synchronization with patient-specific Digital Twins (DTs). In such systems, the freshness of transmitted data, measured by the Age of Information (AoI) is crucial for QoS, realistic interactions, and accurate clinical decisions. However, wireless hospital networks operate under limited bandwidth and time-varying channel conditions while supporting multiple sensors with different levels of clinical importance. Conventional transmission scheduling approaches are not well suited for such dynamic environments, particularly when sudden physiological anomalies require immediate prioritization. This paper presents a reinforcement learning based transmission scheduling framework to minimize AoI in Metaverse-assisted healthcare networks. A Q-learning agent dynamically selects sensors for transmission and allocates bandwidth based on network conditions, queue states and clinical priorities. A lightweight statistical anomaly detection module adjusts sensor priority levels when irregular physiological patterns are detected, ensuring timely delivery of critical health data. Simulations experiments using a publicly available physiological signal datasets show significant AoI reduction, demonstrating an effective AI-based strategy for Metaverse-enabled healthcare networks.