Application of Artificial Intelligence in Improving Diagnostic Accuracy in Radiography

Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in enhancing diagnostic accuracy in radiography. In recent years, the healthcare industry has witnessed rapid advancements in technology, with AI emerging as a promising tool to improve the efficiency and accuracy of radiological imaging interpretation. The aim of this study is to investigate the potential benefits, challenges, and implications of integrating AI algorithms into radiography practice. The introduction provides an overview of the research area and highlights the significance of utilizing AI in radiography. The background of the study elucidates the existing literature on AI applications in healthcare and radiology, emphasizing the need for further research in this domain. The problem statement identifies the limitations of traditional radiography methods and underscores the potential for AI to address these challenges. The objectives of the study are outlined to investigate the impact of AI on diagnostic accuracy, patient outcomes, and workflow efficiency in radiography settings. The scope of the study delineates the boundaries within which the research will be conducted, focusing on specific AI algorithms and their implementation in radiological practice. The significance of the study emphasizes the potential contributions of AI to improving healthcare delivery and patient care. The literature review delves into ten key areas, including the evolution of AI in healthcare, applications of AI in radiography, challenges and opportunities of AI integration, and ethical considerations. The research methodology chapter elaborates on the study design, data collection methods, AI model development, validation techniques, and statistical analyses employed in the research process. The findings chapter presents a detailed discussion of the results obtained from the application of AI algorithms in radiography practice. The analysis includes comparisons between AI-assisted diagnoses and traditional radiological interpretations, highlighting the improvements in diagnostic accuracy and efficiency achieved through AI integration. In conclusion, this thesis summarizes the key findings, implications, and recommendations arising from the study. The potential benefits of AI in enhancing diagnostic accuracy, reducing interpretation errors, and optimizing radiography workflows are underscored. The study contributes to the growing body of literature on AI applications in healthcare and radiology, emphasizing the transformative potential of AI technologies in improving patient care and healthcare outcomes. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Healthcare, Machine Learning, Medical Imaging, Technology Integration.