Thesis Abstract

Abstract
The integration of artificial intelligence (AI) in radiography has revolutionized the field of medical imaging by enhancing image analysis and diagnosis. This thesis explores the application of AI in radiography for image analysis and diagnosis, focusing on its implications for healthcare professionals and patients. The study aims to investigate the effectiveness of AI algorithms in improving diagnostic accuracy, reducing interpretation time, and enhancing patient care outcomes. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The definition of key terms related to AI in radiography is also provided to establish a common understanding of the concepts discussed throughout the thesis. Chapter Two presents a comprehensive literature review that examines existing studies, research findings, and advancements in the field of AI in radiography. Ten key themes related to the application of AI in image analysis and diagnosis are explored, providing a foundation for the empirical research conducted in this thesis. Chapter Three outlines the research methodology employed in this study, detailing the research design, data collection methods, sample selection, data analysis techniques, and ethical considerations. The chapter also discusses the challenges and limitations encountered during the research process, as well as the strategies adopted to address them. Chapter Four presents a detailed discussion of the research findings, focusing on the effectiveness of AI algorithms in radiography for image analysis and diagnosis. The chapter examines the impact of AI on diagnostic accuracy, interpretation time, patient outcomes, and the overall quality of healthcare delivery in radiology departments. Chapter Five concludes the thesis by summarizing the key findings, implications, and recommendations for future research and practice. The study highlights the potential of AI in radiography to transform medical imaging and improve patient care outcomes, emphasizing the importance of continued research and innovation in this rapidly evolving field. In conclusion, the application of artificial intelligence in radiography for image analysis and diagnosis holds great promise for enhancing diagnostic accuracy, improving patient care outcomes, and advancing the field of medical imaging. This thesis contributes to the growing body of knowledge on AI in radiography and provides valuable insights for healthcare professionals, researchers, and policymakers seeking to leverage AI technologies to enhance healthcare delivery and patient outcomes.

Thesis Overview