CLOUD-INTEGRATED ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MRI ANALYSIS: ADVANCING RADIOLOGICAL DIAGNOSTICS THROUGH AUTOMATED SOLUTIONS

Abstract

This article presents a comprehensive framework for integrating artificial intelligence and cloud technology into magnetic resonance imaging (MRI) analysis workflows. The proposed system addresses the growing challenges faced by radiologists in managing increasing imaging volumes while maintaining diagnostic accuracy. By leveraging advanced machine learning algorithms, including generative adversarial networks, the framework automates preliminary image analysis while seamlessly integrating with existing Picture Archiving and Communication Systems (PACS) and Electronic Health Records (EHR). The methodology encompasses data preparation, model development, clinical workflow integration, and continuous performance monitoring, while adhering to stringent healthcare privacy regulations. Results demonstrate significant improvements in diagnostic efficiency and workflow optimization, with enhanced image quality and automated abnormality detection. The framework provides a scalable solution for healthcare institutions seeking to modernize their radiological services while maintaining high standards of patient care. This article contributes to the growing body of research on AI applications in medical imaging and provides practical insights for healthcare providers implementing similar systems.