Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in radiography to enhance diagnostic accuracy in medical imaging. The integration of AI technologies in radiography has shown promising potential to improve the speed and accuracy of diagnoses, thus benefiting both healthcare providers and patients. The study aims to investigate the effectiveness of AI algorithms in analyzing radiographic images, identifying abnormalities, and assisting radiologists in making more accurate diagnostic decisions. The research methodology involves a comprehensive literature review to examine existing studies on AI in radiography, focusing on the various AI techniques and algorithms used, as well as their impact on diagnostic accuracy. Additionally, primary research will be conducted to collect data on the performance of AI systems compared to traditional radiographic interpretation methods. Chapter 1 provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. Chapter 2 presents a detailed literature review covering ten key areas related to AI in radiography, including AI techniques, applications, benefits, challenges, and future trends. Chapter 3 outlines the research methodology, including the research design, data collection methods, sample population, data analysis techniques, and ethical considerations. The chapter also discusses the tools and technologies used in the study, such as AI algorithms and image processing software. Chapter 4 presents a comprehensive discussion of the research findings, analyzing the performance of AI algorithms in radiography and comparing them to traditional diagnostic methods. The chapter also explores the implications of AI integration for radiology practice, including its potential impact on workflow efficiency and patient outcomes. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications for radiography practice, and highlighting areas for future research. The study contributes to the growing body of literature on AI in radiography and provides valuable insights into the potential benefits and challenges of integrating AI technologies into clinical practice. Overall, this thesis seeks to advance the understanding of how AI can be effectively utilized in radiography to enhance diagnostic accuracy, improve patient care, and optimize healthcare outcomes. The findings have important implications for radiologists, healthcare providers, and researchers seeking to leverage AI technologies for improved diagnostic decision-making in medical imaging.