Green AI in Oncology: Toward Energy-Efficient and Accurate Cancer Diagnostics

Abstract

The concept of artificial intelligence (AI) has become an exciting technology in the field of oncology, allowing us to diagnose cancer more accurately and in time, using superior imaging, genomics, and pathology data. Nevertheless, the growing use of computationally intensive deep learning models creates serious doubts regarding energy consumption, environmental sustainability, and accessibility within resource-limited healthcare systems. A way towards more sustainable medical innovation is through green AI, which prioritizes energy use without losing diagnostic quality. In this analysis, the intersection of Green AI and oncology is examined through the existing diagnostic applications, assessment of energy-saving algorithm implementation, and trade-offs between computational cost and diagnostic accuracy. Results indicate that optimized architectures, model compression, edge computing, and federated learning can significantly lower the power consumption of oncology-oriented AI systems without causing a significant drop in accuracy. Green AI principles can not only contribute to environmental sustainability but also increase scalability and equity in cancer care. Future directions encompass the establishment of energy-performance standards, policy frameworks to support sustainable adoption of AI in health care, and clinical validation of lightweight models but robust models. The current research highlights that oncology needs a paradigm shift to energy-aware AI solutions that can provide both precision and sustainability in cancer diagnostics.