AIoMT-Driven Secure and Green Medical Image Processing for Sustainable Healthcare Supply Chains

Healthcare supply chains manage increasing volumes of high-resolution computed tomography (CT) scans daily. The scan of medical images uses storage infrastructure increases transmission costs and elevates carbon emissions. Traditional compression methods achieve limited ratios for medical images, while current deep-learning approaches require intensive computational resources, making them unsuitable for sustainable healthcare operations. This study introduces ESGC-Net, an Artificial Intelligence of Medical Things (AIoMT) driven framework that optimizes secure medical image distribution throughout the healthcare supply chain while reducing environmental impact. Our approach integrates deep spatiotemporal learning with subset-based coding to create an environmentally sustainable compression pipeline. The framework processes CT image sequences using regional healthcare edge devices, enabling efficient data flow from imaging centers to cloud storage and clinical access points. The AIoMT integration allows for distributed processing at multiple points in the supply chain, reducing data transfer volumes while maintaining data security through adaptive encryption that protects patient privacy across all supply chain nodes. Experimental results demonstrate that ESGC-Net attains a 4.85 compression ratio while preserving diagnostic image quality, reducing processing time by 41.0% and energy consumption by 31.5% compared to existing methods. The subset-optimal encoding technique decreases encoding time by 47.8%, enabling faster image transmission between healthcare facilities.