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

Abstract

The early detection and precision care have been advanced greatly by the increasing implementation of artificial intelligence (AI) in cancer diagnosis, especially in computational pathology and medical imaging. Nevertheless, there is a significant issue with the massive deployment of large-scale deep learning models in terms of environmental and energy concerns. Green AI as a concept provides a sustainable system, which is energy-efficient, computational efficient, and less carbon footprint without impacting clinical accuracy. The paper examines sustainable AI-enabled cancer diagnostics methods such as lightweight neural networks, model compression, transfer learning, and federated learning, which minimizes both the training and deployment expenses. Optimized deep learning procedures in computational pathology improve tumor detection and classification at minimal energy usage. Likewise, in medical imaging, resource-conscious models to interpret MRI, CT and ultrasound images show how resource-consciousness in computation may be achieved without reducing diagnostic accuracy. The insights of the case indicate some practical applications of low-power AI systems and renewable-powered infrastructures in medical diagnostics. Although there have been some encouraging developments, there are still difficulties in trying to optimize patient safety and sustainability objectives, guaranteeing that models are reproducible, and expand Green AI systems between healthcare systems. Finally, the concept of sustainability within AI-powered cancer diagnostics promotes environmental accountability as well as the inclusion of fair and equitable healthcare innovation.