Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
In recent years, the healthcare industry has witnessed a rapid advancement in technology, particularly in the field of radiography. The integration of Artificial Intelligence (AI) has shown great potential in revolutionizing the way medical imaging is interpreted and diagnosed. This thesis focuses on the implementation of AI in radiography to enhance diagnostic accuracy and ultimately improve patient outcomes. The introduction provides an overview of the background of the study, highlighting the growing importance of AI in healthcare and its potential impact on radiography. The problem statement identifies the current challenges faced in traditional radiography practices, such as human error and variability in interpretations, which can lead to misdiagnosis and delayed treatment. The objectives of the study aim to investigate how AI can be effectively integrated into radiography processes to improve diagnostic accuracy, streamline workflow, and enhance overall efficiency. The limitations of the study are also discussed, acknowledging potential constraints such as data availability, technical limitations, and ethical considerations. The scope of the study outlines the specific areas within radiography where AI can be applied, including image analysis, pattern recognition, and decision support systems. The significance of the study lies in its potential to transform radiography practices, leading to more accurate and timely diagnoses, improved patient care, and cost-effective healthcare delivery. The literature review chapter delves into existing research and developments in AI applications in radiography, covering topics such as machine learning algorithms, deep learning models, and computer-aided diagnosis systems. The chapter provides a comprehensive analysis of the current state of AI in radiography and identifies gaps in the literature that this study aims to address. The research methodology chapter details the approach taken to implement AI in radiography, including data collection methods, model development, validation techniques, and performance evaluation metrics. The chapter also discusses ethical considerations, data privacy issues, and potential biases in AI algorithms. The discussion of findings chapter presents the results of the study, showcasing how AI has improved diagnostic accuracy in radiography through case studies, comparative analyses, and performance metrics. The chapter also highlights the challenges encountered during the implementation process and offers recommendations for future research and practice. In conclusion, this thesis demonstrates the potential of AI in radiography to enhance diagnostic accuracy, improve patient outcomes, and streamline healthcare delivery. By leveraging the power of AI technologies, radiography practitioners can make more informed decisions, reduce diagnostic errors, and ultimately provide better care for patients. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Machine Learning, Deep Learning, Healthcare, Image Analysis, Decision Support Systems.