Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) in radiography to enhance diagnostic accuracy in medical imaging. The integration of AI technology in radiography has the potential to revolutionize the field by providing radiologists with advanced tools for image analysis, interpretation, and diagnosis. The study examines the background and significance of AI in radiography, highlighting the current challenges faced in traditional radiological practices and the potential benefits of AI implementation. The research methodology consists of a comprehensive literature review that covers ten key areas related to AI in radiography, including machine learning algorithms, deep learning techniques, image segmentation, and computer-aided diagnosis systems. The review synthesizes existing knowledge and identifies gaps in the literature, paving the way for the development of a novel AI solution tailored to the specific needs of radiography. The methodology section outlines the research design, data collection methods, and analytical techniques employed in the study. It discusses the selection of datasets, model training procedures, and evaluation metrics used to assess the performance of the AI system in radiographic image analysis. The chapter also addresses ethical considerations, data privacy concerns, and potential limitations of the research. Chapter four presents a detailed discussion of the findings obtained from the implementation of AI in radiography. It analyzes the impact of AI on diagnostic accuracy, efficiency, and workflow optimization in radiological practice. The chapter explores the strengths and limitations of the AI system, highlighting areas for improvement and future research directions. The conclusion chapter summarizes the key findings of the study and provides insights into the implications of AI implementation in radiography. It discusses the potential benefits of AI technology for radiologists, patients, and healthcare systems, emphasizing the importance of ongoing research and development in this rapidly evolving field. Overall, this thesis contributes to the growing body of knowledge on AI in radiography and provides a foundation for further research and innovation in the field. By harnessing the power of AI technology, radiologists can improve diagnostic accuracy, enhance patient care, and advance the practice of medical imaging for the benefit of society as a whole.

Thesis Overview