Application of Artificial Intelligence in Radiography: A Comparative Analysis of Diagnostic Accuracy

Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in the field of radiography, focusing on its comparative analysis of diagnostic accuracy. The integration of AI technologies into radiography has shown promising potential to enhance the efficiency and accuracy of diagnostic processes. This research aims to investigate the effectiveness of AI algorithms in comparison to traditional radiographic methods, with a specific emphasis on diagnostic accuracy. The Introduction sets the stage by providing an overview of the increasing role of AI in healthcare, particularly in radiology. It highlights the significance of this study in improving diagnostic outcomes through the utilization of AI technologies. The Background of Study delves into the evolution of radiography and the emergence of AI as a disruptive technology in medical imaging. It discusses previous research studies and developments in AI applications within radiography, laying the foundation for the current investigation. The Problem Statement identifies the existing gaps in research regarding the comparative analysis of diagnostic accuracy between AI-based and traditional radiographic methods. It underscores the need to address these gaps to better understand the potential benefits and limitations of AI in radiography. The Objectives of Study outline the specific goals of this research, including evaluating the diagnostic accuracy of AI algorithms, comparing them to conventional radiographic approaches, and assessing the impact of AI on clinical decision-making in radiography. The Limitations of Study acknowledge the constraints and challenges that may impact the research outcomes, such as sample size limitations, data quality issues, and technological constraints inherent in AI applications. The Scope of Study defines the boundaries within which the research will be conducted, specifying the types of AI algorithms, radiographic modalities, and diagnostic scenarios that will be included in the comparative analysis. The Significance of Study emphasizes the potential implications of the research findings on advancing the field of radiography, improving patient outcomes, and guiding future developments in AI integration within healthcare settings. The Structure of the Thesis provides a roadmap of the chapters and sections that will be covered in the research, outlining the sequential flow of the study from introduction to conclusion. In conclusion, this thesis aims to contribute valuable insights into the comparative analysis of diagnostic accuracy between AI-based and traditional radiographic methods. By evaluating the efficacy of AI algorithms in enhancing diagnostic outcomes, this research seeks to inform healthcare professionals, policymakers, and researchers on the potential benefits and challenges associated with the integration of AI in radiography.