Driving Efficiency: Renewable Energy Integration Elevates Diagnostic Imaging Analysis with RPA and Deep Learning


  • Austin Carl, Bruce Juan Department of Mechanical Engineering, Yale University USA


Renewable energy, diagnostic imaging analysis, Robotic Process Automation (RPA), Deep Learning, healthcare efficiency, sustainability, workflow optimization, artificial intelligence (AI), renewable energy integration, healthcare innovation


This paper explores the transformative impact of integrating renewable energy with Robotic Process Automation (RPA) and Deep Learning technologies to enhance diagnostic imaging analysis in healthcare. Renewable energy sources, such as solar and wind power, offer sustainable alternatives to conventional energy grids, reducing carbon emissions and operational costs. By harnessing renewable energy, coupled with the automation capabilities of RPA and the analytical power of Deep Learning, healthcare facilities can drive efficiency in diagnostic imaging workflows. This paper investigates the synergistic effects of renewable energy integration, RPA automation, and Deep Learning-based analysis, highlighting their benefits in terms of sustainability, workflow optimization, and diagnostic accuracy.