Thesis Abstract

Abstract
The integration of artificial intelligence (AI) in radiography has the potential to revolutionize the field by enhancing diagnostic accuracy and efficiency. This thesis explores the implementation of AI technology in radiography to improve diagnostic accuracy. The study aims to investigate the impact of AI on radiographic image analysis, interpretation, and decision-making processes. 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 chapter also includes definitions of key terms related to AI in radiography. Chapter Two presents a comprehensive literature review comprising ten key areas related to the implementation of AI in radiography. The review explores existing studies, technologies, and advancements in AI applications within the field of radiography, highlighting the benefits and challenges associated with AI integration. Chapter Three outlines the research methodology employed in this study, including research design, data collection methods, sampling techniques, data analysis procedures, ethical considerations, and limitations of the study. The chapter also discusses the tools and techniques used for AI implementation in radiography. Chapter Four presents a detailed discussion of the findings derived from the research. This chapter analyzes the impact of AI on diagnostic accuracy in radiography, comparing AI-assisted diagnoses with traditional methods. The discussion also addresses the challenges and opportunities associated with the integration of AI technology in radiographic practices. Chapter Five provides a conclusion and summary of the thesis, highlighting the key findings, implications, and recommendations for future research and practice. The chapter emphasizes the significance of AI in radiography for enhancing diagnostic accuracy and improving patient outcomes. In conclusion, this thesis underscores the importance of implementing AI in radiography to enhance diagnostic accuracy and efficiency. The findings suggest that AI technology has the potential to revolutionize radiographic practices, leading to more accurate and timely diagnoses. This research contributes to the ongoing discourse on the integration of AI in healthcare and underscores the need for further exploration of AI applications in radiography.

Thesis Overview