Application of Artificial Intelligence in Radiography for Improved Diagnostic Imaging

Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in radiography to enhance diagnostic imaging in healthcare settings. The use of AI technologies in radiography has shown great potential in improving the accuracy and efficiency of diagnostic procedures. The research focuses on the development and implementation of AI algorithms and models to assist radiographers and clinicians in interpreting medical images effectively. The study investigates the benefits, challenges, and implications of integrating AI into radiography practices, considering ethical, technical, and practical aspects. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of AI in radiography and its potential impact on diagnostic imaging. Chapter 2 comprises a comprehensive literature review that examines existing studies, research, and advancements in the field of AI and radiography. The review covers ten key areas, including AI applications in medical imaging, machine learning algorithms, deep learning techniques, image segmentation, feature extraction, computer-aided diagnosis, radiomics, data integration, quality assurance, and future trends in AI-assisted radiography. Chapter 3 details the research methodology employed in the study, including the research design, data collection methods, data analysis techniques, AI algorithm development, model evaluation, and validation procedures. The chapter outlines the steps taken to investigate the effectiveness and reliability of AI tools in enhancing diagnostic imaging practices in radiography. In Chapter 4, the discussion of findings delves into the results obtained from the research experiments, case studies, and data analysis conducted during the study. The chapter highlights the strengths, weaknesses, opportunities, and threats associated with the application of AI in radiography, providing insights into the practical implications and potential challenges of implementing AI technologies in clinical settings. Chapter 5 presents the conclusion and summary of the thesis, summarizing the key findings, implications, recommendations, and future research directions. The chapter highlights the significance of integrating AI into radiography for improved diagnostic accuracy, patient outcomes, and healthcare delivery. The study emphasizes the need for continuous research, development, and adoption of AI technologies to advance the field of radiography and enhance the quality of healthcare services. In conclusion, this thesis contributes to the growing body of knowledge on the application of Artificial Intelligence in radiography for improved diagnostic imaging. The research findings underscore the transformative potential of AI technologies in revolutionizing radiography practices and shaping the future of healthcare diagnostics. By leveraging AI tools and techniques, radiographers and clinicians can enhance their decision-making processes, optimize workflow efficiency, and deliver better patient care in the rapidly evolving healthcare landscape.