Apply AI with Edge Computing: Creating Low-Latency Inference Possible for Life-or-Death Applications in Healthcare and Industry

Abstract

Healthcare and industrial automation real-time decision-making are beyond the capabilities of traditional cloud-centric computing infrastructures. Delays caused by centralized infrastructure, bandwidth constraints, and latency can be fatal in some cases. In order to better handle time-sensitive applications, this study enhances the AI-edge computing architecture for low-latency inference. The proposed method avoids transferring data to remote servers by processing it locally using AI models stored on edge devices. This approach considerably improves system reliability while decreasing reaction time in networks that are unreliable or constrained. For quick and precise decisions, the design uses distributed edge nodes, real-time data processing, and lightweight deep learning models. Medical imaging analysis, patient monitoring, and emergency diagnoses are all supported by the system. It allows for the detection of problems, safety monitoring, and predictive maintenance in industrial settings. Hardware acceleration and model compression improve the performance of edge devices with limited resources. The suggested method reduces latency while keeping accuracy, according to the experimental results. This technique improves privacy by reducing data transmission to the cloud. Intelligent, responsive, and dependable systems are the result of this study, which is carried out for vital applications that necessitate rapid judgments on efficiency and safety.