Application of Artificial Intelligence in Radiography for Improved Diagnosis

Thesis Abstract

Abstract
This thesis investigates the application of artificial intelligence (AI) in radiography to enhance the accuracy and efficiency of diagnostic processes. Radiography plays a crucial role in modern healthcare by providing valuable insights into the internal structures of the human body through the use of medical imaging techniques. However, the interpretation of radiographic images can be complex and time-consuming, often requiring specialized expertise. The integration of AI technologies, such as machine learning algorithms and deep learning models, has the potential to revolutionize radiographic interpretation by aiding radiologists in detecting abnormalities, making accurate diagnoses, and improving patient outcomes. Chapter one of the thesis provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The definitions of key terms related to artificial intelligence and radiography are also presented to establish a common understanding of the subject matter. Chapter two presents a comprehensive literature review that examines existing research and developments in the field of AI applications in radiography. The review covers topics such as the evolution of AI in healthcare, the role of AI in medical imaging, current challenges in radiographic interpretation, and recent advancements in AI algorithms for diagnostic purposes. Chapter three details the research methodology employed in this study, including the research design, data collection methods, AI models utilized, and evaluation criteria. The chapter also discusses ethical considerations, data privacy issues, and potential biases associated with AI algorithms in radiography. Chapter four presents the findings of the research, including the performance evaluation of AI models in radiographic interpretation, the comparison of AI-assisted diagnoses with traditional methods, and the impact of AI integration on diagnostic accuracy and efficiency. The chapter also explores the challenges and limitations encountered during the implementation of AI in radiography. Chapter five concludes the thesis by summarizing the key findings, discussing the implications of the research, and offering recommendations for future studies in the field. The conclusion emphasizes the potential of AI technologies to transform radiographic practices and improve healthcare outcomes for patients. In conclusion, this thesis contributes to the ongoing discourse on the integration of artificial intelligence in radiography for enhanced diagnostic capabilities. By leveraging AI technologies, radiologists can optimize their workflow, increase diagnostic accuracy, and ultimately provide better patient care. The findings of this research underscore the importance of continued innovation and collaboration between healthcare professionals and technology experts to harness the full potential of AI in radiography.