Thesis Abstract

Abstract
The advancement of Artificial Intelligence (AI) technology has revolutionized various industries, including healthcare. In the field of radiography, AI has shown promising potential in enhancing diagnostic accuracy and efficiency. This thesis investigates the utilization of AI in radiography for improved diagnostic accuracy. The primary objective of this study is to explore the benefits and challenges of integrating AI technologies into radiography practice and to assess its impact on diagnostic accuracy. The introductory chapter provides an overview of the research study, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. A comprehensive literature review in chapter two examines existing studies and research on the application of AI in radiography, focusing on its role in improving diagnostic accuracy. Chapter three outlines the research methodology employed in this study, including research design, data collection methods, data analysis techniques, and ethical considerations. The methodology chapter also discusses the selection criteria for the sample population and the tools used for data collection and analysis. Chapter four presents a detailed discussion of the findings obtained from the research study. The chapter explores the impact of AI technology on radiography practice and examines the effectiveness of AI algorithms in enhancing diagnostic accuracy. The findings are analyzed in the context of existing literature and provide insights into the potential benefits and challenges of implementing AI in radiography. In the concluding chapter, chapter five, the key findings of the study are summarized, and conclusions are drawn regarding the effectiveness of utilizing AI in radiography for improved diagnostic accuracy. The implications of the study for healthcare practice and future research directions are also discussed. Overall, this thesis contributes to the growing body of knowledge on the integration of AI technology in radiography practice. By examining the benefits and challenges of utilizing AI for improved diagnostic accuracy, this study provides valuable insights that can inform healthcare professionals, policymakers, and researchers in leveraging AI technology to enhance patient care and outcomes in radiography.

Thesis Overview

The project titled "Utilization of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technology into radiography practices to enhance the accuracy and efficiency of diagnostic processes. In recent years, AI has shown great potential in revolutionizing various fields, including healthcare, by offering advanced capabilities in data analysis, pattern recognition, and decision-making. The research will delve into the current challenges faced in traditional radiography practices, such as the time-consuming nature of image analysis, potential human errors in interpretation, and the increasing demand for timely and accurate diagnostics. By incorporating AI algorithms and machine learning models into radiography workflows, the study seeks to address these challenges and improve the overall diagnostic accuracy and efficiency. The project will involve a comprehensive review of existing literature on AI applications in radiography, including studies on image processing, feature extraction, and AI-driven diagnostic tools. By synthesizing the findings from these studies, the research aims to identify the most effective AI methodologies for enhancing diagnostic accuracy in radiography. Moreover, the project will outline a detailed research methodology that includes data collection, AI model development, training, and testing phases. The study will utilize real-world radiographic datasets to train AI models and evaluate their performance in comparison to traditional diagnostic methods. Through rigorous testing and validation processes, the research aims to demonstrate the efficacy and reliability of AI-driven diagnostic solutions in radiography. Furthermore, the project will present a detailed analysis of the findings, highlighting the comparative performance of AI-enabled diagnostic tools against conventional radiography practices. The discussion will address the strengths and limitations of AI technologies in radiography, as well as the potential implications for clinical practice, patient care, and healthcare efficiency. In conclusion, the research on the "Utilization of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" holds significant promise for advancing the field of radiography and improving patient outcomes. By leveraging the power of AI technology, healthcare providers can enhance the accuracy, speed, and reliability of diagnostic processes, ultimately leading to more effective treatment plans and better overall healthcare delivery.