Application of Artificial Intelligence in Radiography: Enhancing Diagnostic Accuracy and Efficiency

Thesis Abstract

**Abstract
** This thesis explores the application of artificial intelligence (AI) in radiography to enhance diagnostic accuracy and efficiency. With the rapid advancements in technology, AI has emerged as a promising tool in various fields, including healthcare. Radiography plays a crucial role in the diagnosis and treatment of various medical conditions, and the integration of AI has the potential to revolutionize this process. The primary objective of this research is to investigate how AI can be leveraged to improve the accuracy and efficiency of radiographic diagnostics. The study begins by providing an overview of the background of AI in healthcare and radiography. It highlights the significance of this research, emphasizing the growing need for advanced technology to support healthcare professionals in making more precise diagnoses. The problem statement identifies the challenges faced in traditional radiographic practices and the limitations that AI can address. The research objectives aim to explore the potential benefits of AI in radiography, including enhancing diagnostic accuracy, reducing errors, and improving overall efficiency. The scope of the study delves into specific aspects of radiography that can benefit from AI integration, such as image analysis, pattern recognition, and decision support systems. A comprehensive literature review presents an in-depth analysis of existing studies, research, and advancements in AI applications in radiography. The review covers ten key areas, including the history of AI in healthcare, current trends in radiographic diagnostics, AI algorithms for image analysis, and the impact of AI on diagnostic accuracy and efficiency. Additionally, it explores the challenges and ethical considerations associated with AI implementation in healthcare settings. The research methodology outlines the approach taken to investigate the application of AI in radiography. It includes details on data collection methods, study design, participants, data analysis techniques, and the tools utilized for the research. The methodology section also discusses the ethical considerations and potential biases that may arise during the study. The findings of the research are presented in chapter four, which offers a detailed discussion of the results obtained from the study. The analysis includes insights into how AI technologies can improve diagnostic accuracy, reduce interpretation errors, and enhance workflow efficiency in radiography. The discussion also addresses the challenges and limitations of AI integration in radiographic practices and highlights areas for further research and development. In conclusion, this thesis summarizes the key findings and implications of integrating AI in radiography to enhance diagnostic accuracy and efficiency. It emphasizes the potential benefits of AI technologies in improving patient outcomes, reducing healthcare costs, and empowering healthcare professionals with advanced tools for precision diagnostics. The study underscores the importance of continued research and innovation in leveraging AI to transform radiographic practices and improve healthcare delivery. Overall, this research contributes to the growing body of knowledge on the application of artificial intelligence in radiography and provides valuable insights for healthcare professionals, researchers, and policymakers seeking to harness the potential of AI technology in advancing diagnostic accuracy and efficiency in radiology practice.