Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) technologies in radiography has revolutionized the field of medical imaging by enhancing diagnostic accuracy and efficiency. This thesis explores the application of AI in radiography to improve diagnostic accuracy, focusing on its impact on healthcare outcomes. The study begins with an introduction to the background of AI in radiography, discussing the rapid advancements in technology and the increasing demand for more precise diagnostic tools. The problem statement highlights the existing challenges in traditional radiography practices, emphasizing the need for AI-driven solutions to enhance accuracy and speed in diagnosis. The objectives of the study are to evaluate the effectiveness of AI algorithms in improving diagnostic accuracy, assess the limitations and challenges associated with AI implementation in radiography, and determine the scope and significance of integrating AI technologies in medical imaging. Through a comprehensive literature review, this thesis examines ten key studies that demonstrate the successful application of AI in radiography, showcasing the potential benefits and limitations of these technologies. The research methodology section outlines the approach taken to conduct this study, including data collection methods, sample selection criteria, and analytical techniques employed. Eight key components are discussed, covering the research design, data sources, data analysis techniques, and ethical considerations in conducting research in the field of AI in radiography. Chapter four presents a detailed discussion of the findings, analyzing the impact of AI technologies on diagnostic accuracy in radiography. The results highlight the potential of AI algorithms to enhance image interpretation, reduce diagnostic errors, and improve patient outcomes. The discussion also addresses the challenges and limitations faced in implementing AI in radiography, such as data privacy concerns, regulatory issues, and the need for continuous training and validation of AI models. In conclusion, this thesis summarizes the key findings and insights gained from the study, emphasizing the significance of integrating AI technologies in radiography to improve diagnostic accuracy and patient care. The study underscores the transformative potential of AI in revolutionizing medical imaging practices and highlights the importance of further research and development in this rapidly evolving field. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Medical Imaging, Healthcare Outcomes.