Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements in recent years, with the integration of artificial intelligence (AI) technologies offering new avenues for improved image analysis and interpretation. This thesis explores the implementation of AI in radiography for enhancing the accuracy and efficiency of image analysis and interpretation processes. The research focuses on developing and evaluating AI algorithms that can assist radiographers in detecting abnormalities, making diagnoses, and providing accurate interpretations of medical images. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter 2 examines existing studies and technologies related to AI in radiography, covering topics such as machine learning algorithms, deep learning models, computer-aided diagnosis systems, and image processing techniques. Chapter 3 outlines the research methodology employed in this study, including data collection methods, AI algorithm development, model training and evaluation techniques, and performance metrics used for assessing the effectiveness of the proposed AI system. The methodology also addresses ethical considerations, data privacy issues, and potential biases in AI-based image analysis. In Chapter 4, the findings of the research are presented and discussed in detail. The results of the experimental evaluation of the AI algorithms for image analysis and interpretation are analyzed, highlighting the strengths, limitations, and potential applications of the developed system. The discussion also addresses the implications of AI integration in radiography practice, including its impact on workflow efficiency, diagnostic accuracy, and patient outcomes. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the contributions of the research to the field of radiography, and outlining recommendations for future studies and practical implementations. The conclusion reflects on the potential benefits and challenges of adopting AI technologies in radiography and emphasizes the importance of continued research and development in this area to enhance healthcare delivery and patient care. In conclusion, this thesis contributes to the growing body of knowledge on the implementation of AI in radiography for image analysis and interpretation. By leveraging AI technologies, radiographers can improve diagnostic accuracy, streamline workflow processes, and enhance patient care outcomes. The findings of this research underscore the potential of AI to revolutionize radiography practice and pave the way for more advanced and efficient healthcare services in the future.

Thesis Overview