Green AI for Cancer Diagnosis: Sustainable Approaches in Computational Pathology and Medical Imaging

Abstract

The growing use of AI in computational pathology and medical imaging has considerably improved cancer detection, early detection, and precision treatment. The huge deployment of large-scale deep learning models does, however, raise serious energy and environmental concerns. Without sacrificing clinical precision, green AI offers a sustainable system that is computationally efficient, uses less energy, and leaves less of a carbon footprint. In order to reduce training and deployment costs, this study explores AI-enabled cancer diagnostics methods that are sustainable. These methods include lightweight neural networks, model compression, transfer learning, and federated learning. Improved tumor identification and classification with minimum energy utilization is achieved by optimized deep learning processes in computational pathology. Medical imaging also demonstrates how resource-conscious computation can be accomplished without compromising diagnostic accuracy with models that interpret MRI, CT, and ultrasound images. If the case's findings hold, medical diagnostics could benefit from renewable-powered infrastructures and low-power AI systems. While there has been progress, there is still a long way to go before we can optimize sustainability and patient safety goals, ensure that models are replicable, and expand Green AI systems across healthcare systems. Sustainability in AI-powered cancer diagnostics encourages both environmental responsibility and the incorporation of equitable healthcare innovation, which brings us to our last point.