Implementation of Artificial Intelligence in Radiography: A Comparative Study

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) into radiography is revolutionizing the field by enhancing diagnostic accuracy, efficiency, and patient care. This thesis presents a comprehensive comparative study on the implementation of AI in radiography, aiming to evaluate its impact on radiographic practices and outcomes. The research explores the application of AI technologies in radiography, such as machine learning algorithms, deep learning models, and computer-aided diagnosis systems. The study begins with an introduction to the role of AI in radiography, highlighting its potential benefits and challenges. The background of the study provides a contextual understanding of the current landscape of radiography and the growing influence of AI technologies. The problem statement identifies the gaps and limitations in existing radiographic practices that AI can address, leading to the formulation of research objectives that guide the comparative analysis. A detailed literature review examines relevant studies and research findings on AI in radiography, encompassing topics such as image interpretation, diagnostic accuracy, workflow optimization, and patient outcomes. The research methodology section outlines the approach taken to compare traditional radiographic practices with AI-enabled techniques, including data collection, analysis methods, and evaluation criteria. The findings of the comparative study are presented in the discussion chapter, offering insights into the performance, accuracy, and efficiency of AI-based radiographic processes compared to conventional methods. The discussion also addresses the challenges and limitations encountered in the implementation of AI in radiography and proposes potential solutions and future directions for research and practice. In conclusion, the thesis summarizes the key findings of the comparative study and highlights the significance of integrating AI technologies in radiography. The study underscores the potential of AI to enhance diagnostic capabilities, improve workflow efficiency, and ultimately enhance patient care in radiography. The research contributes to the growing body of knowledge on AI applications in healthcare and provides valuable insights for radiographers, healthcare providers, and policymakers seeking to leverage AI for better radiographic practices. Keywords Artificial Intelligence, Radiography, Machine Learning, Diagnostic Accuracy, Comparative Study, Healthcare Technology.