Implementation of Artificial Intelligence in Radiography for Improved Diagnosis Accuracy

Thesis Abstract

Abstract
Radiography is an essential tool in medical imaging for diagnosing and monitoring a wide range of health conditions. With the advancements in technology, artificial intelligence (AI) has emerged as a promising approach to enhance the accuracy and efficiency of radiography diagnosis. This thesis explores the implementation of AI in radiography to improve diagnosis accuracy. The study begins with an introduction to the background of the research, highlighting the significance and relevance of integrating AI into radiography practices. The problem statement identifies the existing challenges in traditional radiography diagnosis, such as human error, subjective interpretations, and time-consuming processes. The objective of the study is to investigate how AI can address these challenges and improve the accuracy of radiography diagnosis. The limitations and scope of the study are also outlined to provide a clear understanding of the research boundaries. A comprehensive literature review is conducted in Chapter Two to explore the current state of AI applications in radiography, including image recognition, pattern analysis, and diagnostic decision support systems. The review synthesizes findings from various studies to establish the theoretical framework for implementing AI in radiography. Chapter Three details the research methodology employed in this study, including data collection methods, AI algorithms utilized, and evaluation metrics. The methodology section discusses the steps taken to train and test AI models using radiography datasets, ensuring the robustness and reliability of the results. In Chapter Four, the findings of the study are presented and discussed in detail. The results showcase the effectiveness of AI in improving the accuracy of radiography diagnosis, reducing interpretation errors, and enhancing workflow efficiency. The discussion delves into the implications of these findings for radiography practice and the potential benefits of integrating AI into clinical settings. The thesis concludes with Chapter Five, summarizing the key findings and contributions of the research. The conclusion highlights the significance of implementing AI in radiography for improved diagnosis accuracy and patient outcomes. Recommendations for future research and practical implications for healthcare professionals are also provided. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in radiography and its potential to revolutionize diagnostic practices. By leveraging AI technology, radiographers can enhance their diagnostic capabilities, improve patient care, and ultimately advance the field of medical imaging.