Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
Advancements in technology have revolutionized the field of radiography, offering new opportunities to enhance diagnostic accuracy and patient care. This thesis explores the application of artificial intelligence (AI) in radiography to improve diagnostic accuracy. The study investigates how AI technologies can be integrated into radiography practices to assist radiographers in interpreting medical images more effectively and efficiently. The research begins with an introduction to the topic, providing a background of the study and highlighting the problem statement. The objectives of the study are outlined, focusing on the potential benefits of AI in radiography. Limitations and the scope of the study are also discussed, along with the significance of implementing AI in radiography practices. The structure of the thesis is presented, guiding the reader through the content. Chapter two conducts a comprehensive literature review, exploring existing research on the application of AI in radiography. The review covers topics such as machine learning algorithms, image analysis techniques, and the integration of AI systems in radiology departments. Key findings from the literature review provide insights into the current state of AI in radiography and identify gaps for further research. Chapter three details the research methodology employed in this study. The methodology includes data collection methods, sample selection criteria, and the implementation of AI algorithms in radiography practice. Various aspects of the research process are discussed, including data analysis techniques and the evaluation of AI performance in diagnostic tasks. Chapter four presents a detailed discussion of the research findings, analyzing the impact of AI on diagnostic accuracy in radiography. Case studies and experimental results are presented to demonstrate the effectiveness of AI technologies in assisting radiographers with image interpretation. The discussion also addresses challenges and limitations encountered during the research process. In the concluding chapter, the study summarizes the key findings and implications of integrating AI in radiography for improved diagnostic accuracy. The benefits of AI technologies in enhancing patient care and radiographer efficiency are highlighted. Recommendations for future research and the implementation of AI in clinical practice are provided, emphasizing the potential for AI to transform radiography practices. Overall, this thesis contributes to the growing body of research on the application of artificial intelligence in radiography. By exploring the potential of AI technologies to enhance diagnostic accuracy, this study aims to promote the adoption of AI systems in radiology departments to improve patient outcomes and advance the field of radiography.