Thesis Abstract

Abstract
The field of radiography has experienced significant advancements with the incorporation of artificial intelligence (AI) technologies, aiming to enhance diagnostic accuracy and improve patient outcomes. This thesis explores the application of AI in radiography for improved diagnostic accuracy, focusing on the integration of machine learning algorithms and deep learning techniques into radiological image analysis. The study investigates the potential of AI to assist radiographers in interpreting medical images more accurately and efficiently, thereby facilitating early detection of abnormalities and precise diagnosis of various medical conditions. Chapter One provides an introduction to the research topic, presenting the background of the study, defining the problem statement, outlining the objectives, discussing the limitations and scope of the study, highlighting the significance of the research, and presenting the structure of the thesis. The chapter also includes definitions of key terms relevant to the study. Chapter Two consists of a comprehensive literature review that explores existing research and developments in the application of AI in radiography. Ten key areas are discussed, including the evolution of AI in healthcare, the role of AI in radiological image analysis, the benefits and challenges of AI implementation in radiography, and current trends in AI-assisted diagnosis. Chapter Three focuses on the research methodology employed in this study. It includes detailed descriptions of the research design, data collection methods, AI algorithms utilized, image processing techniques applied, evaluation metrics used to assess diagnostic accuracy, and the validation process of the AI models developed. Additionally, ethical considerations and potential biases in AI algorithms are addressed. Chapter Four presents the findings of the study, discussing the outcomes of implementing AI in radiography for diagnostic accuracy improvement. The chapter provides a detailed analysis of the performance of AI models in detecting and classifying abnormalities in medical images compared to traditional radiographic interpretation methods. Furthermore, the challenges encountered during the implementation of AI in radiography are identified, along with potential solutions and future research directions. Chapter Five serves as the conclusion and summary of the thesis, outlining the key findings, contributions to the field of radiography, implications for clinical practice, and recommendations for further research. The study concludes by emphasizing the significant impact of AI on improving diagnostic accuracy in radiography and the potential for enhancing patient care through the integration of AI technologies. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI in radiography for improved diagnostic accuracy. By leveraging the capabilities of AI algorithms in radiological image analysis, this research aims to advance the field of radiography and ultimately enhance healthcare outcomes for patients.

Thesis Overview