Latency Reduction with Edge AI Deployment across Rural Telemedicine Devices

Abstract

The integration of artificial intelligence into telemedicine has fundamentally transformed the diagnostic capabilities available to remote patient populations. However, the reliance on centralized cloud computing infrastructures introduces substantial transmission latency, which remains a critical barrier in rural areas characterized by constrained network bandwidth and unreliable connectivity. This paper presents a comprehensive benchmark study to predict and evaluate the latency reduction achieved by deploying Edge Artificial Intelligence directly onto rural telemedicine devices. By migrating computational workloads from distant cloud servers to localized edge nodes, healthcare providers can facilitate near real-time diagnostic processing. We design a rigorous methodological framework utilizing a simulated rural network environment to evaluate the performance of various edge devices performing image recognition and physiological data analysis. The benchmark study compares traditional cloud-dependent architectures against edge-native processing systems across multiple simulated network degradation scenarios. The findings indicate that localized edge inference significantly mitigates transmission delays, rendering artificial intelligence applications viable even in severely bandwidth-constrained environments. Furthermore, this research provides predictive models for hardware utilization and processing efficiency, offering a foundational blueprint for future decentralized healthcare networks. Ultimately, shifting computational resources to the network periphery empowers rural clinics with robust, uninterrupted, and instantaneous diagnostic support, thereby bridging the geographical healthcare divide.