Thesis Abstract

Abstract
The rapid advancements in technology have significantly impacted the field of radiography, leading to the exploration of innovative approaches to enhance diagnostic accuracy. This thesis investigates the application of Artificial Intelligence (AI) in radiography to improve diagnostic accuracy. The study focuses on leveraging AI algorithms to analyze medical imaging data and assist radiographers in making more precise and timely diagnoses. The research begins with an introduction that outlines the background of the study, identifies the problem statement, and presents the objectives, limitations, scope, significance, structure of the thesis, and definition of terms. A comprehensive literature review in Chapter Two examines existing studies on the use of AI in radiography, highlighting the benefits, challenges, and potential areas for improvement. Chapter Three details the research methodology, including data collection methods, AI algorithm selection criteria, data analysis techniques, and evaluation metrics. The methodology section also discusses the ethical considerations and limitations of the study. Chapter Four presents the findings of the research, showcasing the effectiveness of AI algorithms in improving diagnostic accuracy compared to traditional methods. The discussion delves into the implications of these findings for the field of radiography and explores potential future research directions. Finally, Chapter Five offers a conclusion and summary of the thesis, emphasizing the importance of integrating AI technologies into radiography practice to enhance diagnostic accuracy and patient outcomes. The study concludes by proposing recommendations for further research and practical implementation of AI in radiography. Overall, this thesis contributes to the growing body of knowledge on the application of AI in radiography and underscores its potential to revolutionize diagnostic practices in healthcare settings. By harnessing the power of AI algorithms, radiographers can achieve higher accuracy rates, optimize workflow efficiency, and ultimately improve patient care outcomes.

Thesis Overview