Application of Artificial Intelligence in Improving Diagnostic Accuracy in Radiography

Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) to enhance diagnostic accuracy in the field of Radiography. As medical imaging technology continues to advance, the integration of AI tools presents a promising opportunity to improve the efficiency and precision of radiological interpretations. The primary objective of this study is to investigate the impact of AI algorithms on diagnostic accuracy in radiography, with a focus on identifying abnormalities and optimizing patient care outcomes. The research methodology employed in this study includes a comprehensive literature review to analyze existing AI applications in radiography and their effectiveness in enhancing diagnostic accuracy. Various AI techniques such as machine learning, deep learning, and neural networks will be explored to understand their potential in assisting radiologists in interpreting medical images more accurately and rapidly. Chapter four of this thesis discusses the findings from the study, highlighting the benefits and limitations of integrating AI technology into radiography practice. The results indicate that AI algorithms have the potential to improve diagnostic accuracy by assisting radiologists in identifying subtle abnormalities, reducing interpretation errors, and enhancing workflow efficiency. In conclusion, the application of AI in radiography shows great promise in enhancing diagnostic accuracy and improving patient care outcomes. By leveraging AI tools to assist radiologists in image interpretation, healthcare providers can achieve more precise diagnoses, leading to better treatment planning and patient outcomes. This research contributes to the growing body of knowledge on the integration of AI in medical imaging and underscores the importance of continued research and development in this area to advance healthcare practices. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Medical Imaging, Machine Learning, Deep Learning, Neural Networks, Healthcare Technology.