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.

Thesis Overview

The research project titled "Implementation of Artificial Intelligence in Radiography: A Comparative Study" aims to investigate the integration of artificial intelligence (AI) technologies in the field of radiography. Radiography is a crucial aspect of medical imaging that plays a significant role in the diagnosis and treatment of various medical conditions. With the rapid advancements in AI, there is a growing interest in exploring how AI can enhance and optimize the practice of radiography. This comparative study will examine the current practices in radiography and evaluate the potential benefits of incorporating AI technologies into these practices. By comparing traditional radiography techniques with AI-enhanced approaches, the research aims to identify the strengths and limitations of each method and determine the impact of AI on the quality, efficiency, and accuracy of radiographic imaging. Key areas of focus in this study include the utilization of AI algorithms for image analysis, interpretation, and diagnosis in radiography. By analyzing and comparing the performance of AI systems with human radiologists, the research seeks to assess the reliability and effectiveness of AI in assisting or replacing traditional radiographic practices. The study will also explore the challenges and limitations associated with the implementation of AI in radiography, such as data privacy concerns, regulatory requirements, and the need for specialized training for healthcare professionals. By addressing these issues, the research aims to provide insights and recommendations for the successful integration of AI technologies in radiography practice. Overall, this research project seeks to contribute to the advancement of radiography by exploring the potential benefits and challenges of implementing AI technologies in the field. By conducting a comparative analysis of traditional radiography methods and AI-enhanced approaches, the study aims to provide valuable insights that can inform future developments in the integration of AI in radiography practice.