Investigating the Use of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy.

Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in the field of Radiography to enhance diagnostic accuracy. The integration of AI technologies in radiography has the potential to revolutionize the field by improving the speed and accuracy of diagnoses, ultimately leading to better patient outcomes. The research aims to investigate the effectiveness of AI algorithms in assisting radiographers in interpreting medical images and making accurate diagnoses. The introduction provides an overview of the research topic, highlighting the increasing importance of AI in healthcare and the potential benefits it offers to the field of radiography. The background of the study delves into the evolution of AI in radiography and the current state of the technology in clinical practice. The problem statement emphasizes the challenges faced by radiographers in interpreting complex medical images accurately and the potential of AI to address these challenges. The objectives of the study focus on evaluating the performance of AI algorithms in analyzing medical images, comparing their accuracy with traditional methods, and assessing the impact of AI on diagnostic outcomes. The limitations of the study are acknowledged, including the availability of data, the complexity of AI algorithms, and potential ethical considerations. The scope of the study outlines the specific areas of radiography that will be examined and the types of AI technologies that will be evaluated. The significance of the study lies in its potential to improve diagnostic accuracy, reduce errors, and enhance patient care in radiography. The structure of the thesis is detailed, providing a roadmap for the reader to navigate through the research findings. Definitions of key terms used throughout the thesis are provided to ensure clarity and understanding. The literature review synthesizes existing research on the use of AI in radiography, covering topics such as machine learning algorithms, deep learning techniques, and AI applications in medical imaging. The research methodology outlines the approach taken to collect and analyze data, including the selection of AI models, image datasets, and evaluation metrics. The discussion of findings presents the results of the study, comparing the performance of AI algorithms with traditional methods and assessing their impact on diagnostic accuracy. The conclusion summarizes the key findings of the research, highlighting the strengths and limitations of AI in radiography and proposing recommendations for future research in this area. In conclusion, this thesis contributes to the growing body of knowledge on the use of AI in radiography and its potential to improve diagnostic accuracy. By leveraging the power of AI technologies, radiographers can enhance their capabilities, leading to more accurate diagnoses and improved patient outcomes.