Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in the field of Radiography has shown promising potential for enhancing diagnostic accuracy and improving patient outcomes. This thesis explores the application of AI in Radiography with the aim of enhancing diagnostic accuracy. The research delves into the background of AI technology and its relevance in the medical field, particularly in radiographic imaging. The study identifies the problem of variability and subjectivity in traditional radiographic interpretations, leading to potential diagnostic errors and delays in treatment. The objectives of this research include investigating the capabilities of AI algorithms in analyzing radiographic images, evaluating their performance in detecting abnormalities, and assessing their impact on diagnostic accuracy compared to conventional methods. The limitations of the study are acknowledged, such as the need for large datasets for training AI models and potential challenges in integrating AI systems into existing radiography workflows. The scope of the study focuses on the application of AI in specific radiographic modalities and clinical scenarios to demonstrate its effectiveness in improving diagnostic accuracy. The significance of this research lies in its potential to revolutionize radiographic practice by providing radiologists with advanced tools for faster and more accurate diagnoses, ultimately benefiting patients through timely and precise medical interventions. The structure of the thesis is outlined, encompassing the introductory chapter that sets the context for the research, reviews related literature on AI in radiography, details the research methodology, presents and discusses the findings, and concludes with a summary of key insights. Definitions of key terms related to AI, radiography, and diagnostic accuracy are provided to ensure clarity and understanding throughout the thesis. Chapter Two presents a comprehensive literature review that covers ten key aspects of AI in radiography, including the evolution of AI technology in healthcare, the applications of AI in medical imaging, the challenges and opportunities of AI integration in radiography, and the impact of AI on diagnostic accuracy and patient outcomes. The review synthesizes existing knowledge and identifies gaps in the literature that warrant further investigation. Chapter Three outlines the research methodology, detailing the study design, data collection methods, AI algorithms used, evaluation metrics employed, and statistical analyses conducted. The chapter discusses the ethical considerations involved in using AI for medical purposes and outlines the steps taken to ensure the validity and reliability of the research findings. Chapter Four presents a detailed discussion of the research findings, including the performance of AI algorithms in detecting abnormalities in radiographic images, the comparative analysis of AI-driven diagnoses versus human interpretations, and the implications of AI integration for radiographic practice. The chapter critically analyzes the strengths and limitations of AI systems in radiography and offers insights into their potential applications in clinical settings. Chapter Five concludes the thesis by summarizing the key findings, highlighting the contributions of the research to the field of radiography, and discussing implications for future research and practice. The conclusion underscores the significance of AI in improving diagnostic accuracy in radiographic imaging and emphasizes the need for continued innovation and integration of AI technologies in healthcare settings. In conclusion, this thesis provides a comprehensive exploration of the application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy, offering valuable insights into the transformative potential of AI technology in enhancing radiographic practice and patient care.

Thesis Overview