Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) in radiography to enhance diagnostic accuracy in medical imaging. The integration of AI technologies in radiography has the potential to revolutionize the field by improving the efficiency and effectiveness of diagnostic processes. This research project aims to investigate the impact of AI on radiography practices, focusing on its ability to assist radiologists in interpreting medical images accurately and efficiently. The introduction provides an overview of the research topic, highlighting the importance of incorporating AI into radiography to address the challenges faced by radiologists in interpreting complex medical images. The background of the study delves into the evolution of AI in healthcare and radiography, emphasizing the need for advanced technological solutions to enhance diagnostic accuracy. The problem statement identifies the existing limitations in traditional radiography practices, such as human error, time-consuming manual processes, and the increasing demand for accurate and timely diagnoses. The objective of the study is to evaluate the effectiveness of AI in improving diagnostic accuracy and efficiency in radiography. The literature review chapter presents a comprehensive analysis of existing research studies and developments related to AI in radiography. Ten key themes are explored, including the applications of AI in medical imaging, machine learning algorithms, deep learning techniques, and the advantages and limitations of AI in radiography. The research methodology chapter outlines the research design, data collection methods, and analysis techniques employed in the study. Eight key contents are discussed, including the selection of AI models, dataset preparation, training and testing processes, and performance evaluation metrics. The discussion of findings chapter presents a detailed analysis of the research results, focusing on the impact of AI on diagnostic accuracy in radiography. The findings highlight the effectiveness of AI algorithms in assisting radiologists in detecting abnormalities, classifying medical images, and improving overall diagnostic outcomes. The conclusion and summary chapter provide a comprehensive overview of the research findings and their implications for the field of radiography. The significance of the study is emphasized, highlighting the potential of AI to transform radiography practices and enhance patient care outcomes. In summary, this thesis investigates the implementation of AI in radiography for improved diagnostic accuracy. The findings of this research contribute to the growing body of knowledge on the integration of AI technologies in healthcare and highlight the potential benefits of AI in enhancing diagnostic processes in radiography.