Thesis Abstract

Abstract
The field of radiography plays a crucial role in modern healthcare by providing essential diagnostic imaging services. With the advancement of technology, the integration of artificial intelligence (AI) has brought about significant improvements in diagnostic accuracy and efficiency in radiography. This thesis explores the implementation of AI in radiography to enhance diagnostic accuracy and streamline the workflow in healthcare settings. The study focuses on the development and integration of AI algorithms in radiography processes to assist radiologists in interpreting medical images more accurately and efficiently. The introduction section provides an overview of the background of the study, highlighting the importance of AI in radiography and the potential benefits it offers in improving diagnostic accuracy. The problem statement identifies the existing challenges in traditional radiography practices, such as human error, time-consuming image analysis, and variability in interpretations. The objectives of the study aim to investigate the effectiveness of AI in enhancing diagnostic accuracy, reducing interpretation time, and improving overall workflow efficiency in radiography. The literature review section presents a comprehensive analysis of existing studies and research on the implementation of AI in radiography. It covers topics such as AI algorithms for image analysis, machine learning techniques, deep learning models, and their applications in medical imaging. The review also discusses the advantages and limitations of AI in radiography, as well as the ethical considerations and potential concerns associated with its implementation. The research methodology section outlines the approach taken to investigate the impact of AI implementation in radiography. It includes details on the study design, data collection methods, AI algorithm development, and evaluation processes. The methodology also addresses ethical considerations, data privacy, and quality assurance measures implemented in the study. The discussion of findings section presents the results of the study, highlighting the effectiveness of AI in improving diagnostic accuracy and workflow efficiency in radiography. It discusses the performance of AI algorithms in image analysis, comparison with traditional methods, and implications for clinical practice. The findings also address the challenges and future directions for the implementation of AI in radiography. In conclusion, this thesis provides insights into the implementation of AI in radiography for improved diagnostic accuracy. The study demonstrates the potential of AI algorithms to enhance the quality of radiographic interpretations, reduce errors, and streamline the diagnostic process. The findings contribute to the growing body of research on the integration of AI in healthcare and its impact on radiography practices. Overall, this research underscores the significance of AI in revolutionizing radiography and improving patient care outcomes.

Thesis Overview