Seamless Synergy: Integrating Renewable Energy to Enhance Diagnostic Imaging Analysis with RPA and Deep Learning

Seamless Synergy: Integrating Renewable Energy to Enhance Diagnostic Imaging Analysis with RPA and Deep Learning

Authors

  • Stephen Brandon, Nathan Peter Graduate Student, Department of Electrical & Computer Engineering, Oregon State University, USA

Keywords:

Renewable energy, diagnostic imaging analysis, robotic process automation (RPA), deep learning, healthcare innovation, sustainability, precision medicine, workflow optimization, artificial intelligence (AI), sustainable healthcare

Abstract

This paper explores the seamless synergy achieved by integrating renewable energy with robotic process automation (RPA) and deep learning technologies to enhance diagnostic imaging analysis in healthcare settings. Renewable energy sources, such as solar and wind power, offer sustainable alternatives to conventional energy grids, reducing carbon emissions and operational costs. Through the convergence of renewable energy with RPA automation and deep learning algorithms, healthcare facilities can optimize diagnostic workflows, improve efficiency, and enhance patient care outcomes. This paper investigates the transformative potential of this integrated approach, highlighting its benefits in terms of sustainability, workflow optimization, and diagnostic accuracy.

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Published

2024-02-22

How to Cite

Stephen Brandon, Nathan Peter. (2024). Seamless Synergy: Integrating Renewable Energy to Enhance Diagnostic Imaging Analysis with RPA and Deep Learning. Revista Espanola De Documentacion Cientifica, 18(01), 50–65. Retrieved from http://redc.revistas-csic.com/index.php/Jorunal/article/view/188

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