Artificial Intelligence and Medical Imaging: A Pathway to Sustainable, Data-Driven Healthcare

Abstract

The global healthcare sector stands at a critical juncture, facing unprecedented challenges including aging populations, rising costs, workforce shortages, and the environmental burden of medical waste. Concurrently, it is experiencing a digital revolution, generating vast repositories of data, particularly medical images which constitute nearly 90% of all healthcare data. This paper posits that the strategic integration of Artificial Intelligence (AI) and advanced medical image processing technologies is not merely an incremental improvement but a foundational paradigm shift essential for achieving sustainable, data-driven healthcare. We move beyond technical performance metrics to explore a holistic framework where AI-driven imaging enhances all three pillars of sustainability: economic (through improved efficiency and reduced waste), social (through equitable access and improved outcomes), and environmental (through optimized resource use). This paper provides a detailed analysis of current applications in radiology, pathology, ophthalmology, and cardiology, supported by expanded clinical examples and data tables. We critically examine the "green" implications of computational costs, the ethical imperatives of bias mitigation, and the practical pathways for implementation. Ultimately, we argue that an AI augmented, image centric approach is the most viable pathway to a healthcare system that is not only more accurate and efficient but also profoundly more sustainable and equitable for future generations.