Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in radiography has the potential to revolutionize diagnostic accuracy and enhance healthcare outcomes. This thesis explores the implementation of AI technology in radiography with a focus on improving diagnostic accuracy. The study delves into the background of AI in radiography, highlighting its significance and impact on the healthcare sector. The research methodology employed includes a comprehensive literature review to understand the current landscape of AI applications in radiography. The literature review chapter discusses ten key areas related to AI in radiography, such as machine learning algorithms, deep learning techniques, image recognition, and data processing. These insights provide a foundation for understanding the potential benefits and challenges associated with integrating AI in radiography practices. The research methodology chapter outlines the approach taken to investigate the implementation of AI in radiography. It includes details on data collection methods, sample selection criteria, and analytical techniques used to evaluate the findings. The study aims to provide a detailed analysis of how AI technologies can enhance diagnostic accuracy in radiography and improve patient outcomes. The discussion of findings chapter presents an in-depth analysis of the results obtained from the research. It explores the impact of AI technologies on diagnostic accuracy in radiography, highlighting the strengths and limitations of implementing AI in healthcare settings. The findings shed light on the potential benefits of AI in improving diagnostic accuracy, reducing errors, and enhancing overall patient care. In conclusion, this thesis summarizes the key findings and implications of implementing AI in radiography for improved diagnostic accuracy. The study underscores the importance of leveraging AI technologies to enhance radiographic practices and improve healthcare outcomes. The research contributes to the growing body of knowledge on AI applications in healthcare and provides valuable insights for healthcare practitioners, researchers, and policymakers. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Healthcare, Machine Learning, Deep Learning, Data Processing.

Thesis Overview