Green AI-Driven Low-Power Biochemical Data Processing for Sustainable Healthcare and Drug Discovery

Abstract

The rapid advancements in artificial intelligence (AI) have revolutionized healthcare and drug discovery by enabling faster and more accurate biochemical data analysis. However, traditional AI models often demand substantial computational resources, leading to high energy consumption. This paper explores the application of Green AI principles to develop energy-efficient AI models tailored for biochemical data processing in healthcare and pharmaceutical research. By leveraging ultra-lightweight deep learning architectures, optimized neural networks, and cross-layer optimization techniques, we propose a novel approach to reduce the carbon footprint of AI-driven medical diagnostics, clinical laboratory automation, and drug development. Our study evaluates the effectiveness of these techniques in accelerating drug discovery while maintaining high accuracy in biochemical analysis. We also discuss the integration of Green Chemistry principles in AI-powered pharmaceutical research to enhance sustainability. The findings underscore the potential of low-power AI solutions in making healthcare and drug discovery more efficient, cost-effective, and environmentally sustainable.