Edge AI for Real-Time Medical Image Processing

Abstract

The growing demand for real-time medical image analysis has exposed the limitations of cloud-based systems, particularly in terms of latency, bandwidth consumption, and data privacy concerns. This paper presents an Edge AI-based framework for Real-Time Medical Image Processing, enabling on-device analysis of medical images such as X-rays, CT scans, and MRI data. The proposed system leverages lightweight deep learning models optimized for deployment on edge devices to perform tasks including image classification, segmentation, and anomaly detection. By processing data locally, the framework reduces response time, enhances data security, and minimizes dependency on centralized infrastructure. Model optimization techniques such as pruning, quantization, and knowledge distillation are incorporated to ensure efficient performance on resource-constrained devices. Experimental results demonstrate that the proposed approach achieves near real-time performance with competitive accuracy compared to traditional cloud-based methods. Additionally, integration with prior research in machine learning and healthcare analytics enhances system robustness and scalability. The study highlights the potential of Edge AI in enabling faster clinical decision-making and improving healthcare delivery in remote and resource-limited environments.