Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies

Abstract

Diagnostic imaging analysis plays a pivotal role in modern healthcare, facilitating the accurate detection and characterization of various medical conditions. However, the increasing volume of imaging data coupled with the shortage of radiologists presents significant challenges for healthcare systems worldwide. In response, this research paper explores the integration of Robotic Process Automation (RPA) and Deep Learning technologies to enhance diagnostic imaging analysis. Through a comprehensive literature review, we examine the current landscape of diagnostic imaging and identify opportunities for improvement. The materials and methods section outlines the systematic approach employed in proposed or existing studies, encompassing data acquisition, model development, integration of RPA, and evaluation metrics. Real-world applications and case studies demonstrate the efficacy of RPA-Deep Learning systems in streamlining workflows, reducing turnaround times, and improving diagnostic accuracy. Ethical considerations, including patient privacy and regulatory compliance, are also addressed. In conclusion, the paper advocates for the responsible adoption of RPA and Deep Learning technologies to optimize diagnostic imaging analysis, ultimately leading to improved patient outcomes and enhanced healthcare delivery. This research serves as a roadmap for healthcare providers and researchers seeking to harness the transformative potential of automation and artificial intelligence in diagnostic imaging.