Thesis Abstract

Abstract
This thesis explores the utilization of artificial intelligence (AI) in radiography to enhance the accuracy and efficiency of diagnostic processes in medical imaging. With the increasing demand for timely and accurate diagnoses, the integration of AI technologies holds significant promise for improving healthcare outcomes. The research focuses on the development and implementation of AI algorithms in radiography to assist radiologists in interpreting medical images, thereby reducing diagnostic errors and enhancing patient care. The study begins with an introduction that discusses the background of the research, highlighting the challenges faced in radiography and the potential benefits of incorporating AI technologies. The problem statement identifies the current limitations in traditional diagnostic practices and emphasizes the need for advanced tools to support radiologists in their decision-making processes. The objectives of the study are outlined to guide the research towards achieving specific goals in leveraging AI for improved diagnosis. The literature review presents a comprehensive analysis of existing studies and technologies related to AI in radiography. Ten key themes are explored, including the evolution of AI in healthcare, the application of machine learning algorithms in medical imaging, and the impact of AI on diagnostic accuracy. The review synthesizes current knowledge and identifies gaps in the literature to inform the research methodology. The research methodology section describes the approach taken to develop and evaluate AI models for diagnosing medical conditions using radiographic images. Eight components are detailed, covering data collection, preprocessing, algorithm selection, model training, validation, and performance evaluation. The methodology aims to provide a systematic framework for implementing AI solutions in radiography while ensuring the reliability and robustness of the diagnostic process. The discussion of findings delves into the results obtained from testing the AI models on a dataset of radiographic images. The analysis highlights the performance metrics, such as sensitivity, specificity, and accuracy, to assess the efficacy of the AI algorithms in detecting and classifying medical conditions. The findings are compared with traditional diagnostic methods to evaluate the improvements facilitated by AI technology. In conclusion, this thesis summarizes the key findings and implications of the research, emphasizing the significance of integrating AI in radiography for enhanced diagnostic accuracy and efficiency. The study contributes to the growing body of knowledge on AI applications in healthcare and provides insights into the practical implementation of AI algorithms in medical imaging. The thesis underscores the potential of AI to revolutionize diagnostic practices in radiography and improve patient outcomes in clinical settings. Keywords Artificial Intelligence, Radiography, Medical Imaging, Diagnosis, Machine Learning, Healthcare, Algorithm, Diagnostic Accuracy, Performance Evaluation, Patient Care.

Thesis Overview