Healthcare systems worldwide face increasing pressure to improve diagnostic accuracy while minimizing environmental impact. This study introduces a sustainable artificial intelligence (AI) framework that connects diagnostic technologies with healthcare waste management to create a unified, resource-efficient ecosystem. The research explores how AI-based image recognition models, such as YOLOv5, can enhance early disease detection, and how machine learning algorithms can optimize hospital waste segregation, recycling, and disposal processes. By combining data from diagnostic workflows and waste generation patterns, the framework demonstrates how healthcare institutions can reduce both clinical errors and ecological footprints. The results emphasize that sustainable AI adoption in healthcare requires not only technical innovation but also environmental awareness, cross-disciplinary governance, and ethical data use. This integrated perspective contributes to the emerging field of green healthcare technology and offers guidance for future policy and system design.
Salim Ahmad (Mon,) studied this question.
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