Thesis Abstract

Abstract
The integration of artificial intelligence (AI) into radiography has transformed the field by enhancing diagnostic accuracy, efficiency, and patient care. This comparative study explores the impact of AI on radiography practices, comparing traditional methods with AI-assisted techniques. The research investigates how AI algorithms analyze medical images, improve diagnostic accuracy, reduce interpretation errors, and streamline workflow processes in radiology departments. Through a comprehensive literature review and empirical analysis, this study evaluates the benefits, challenges, and future implications of AI in radiography. The findings highlight the potential of AI to revolutionize radiography practices, improve patient outcomes, and optimize resource utilization. The research methodology includes data collection from radiography departments, interviews with radiographers and AI developers, and case studies of AI implementation in healthcare settings. The results provide valuable insights into the efficacy of AI technologies in radiography and offer recommendations for integrating AI into clinical practice. Overall, this study contributes to the growing body of knowledge on the transformative role of AI in radiography and underscores the importance of continuous innovation and adaptation in the rapidly evolving healthcare landscape.

Thesis Overview