Implementation of Artificial Intelligence in Radiography: A Comparative Study of Automated Image Analysis Techniques

Thesis Abstract

Abstract
This thesis explores the implementation of artificial intelligence (AI) in radiography through a comparative study of automated image analysis techniques. Radiography plays a crucial role in medical diagnostics, and the integration of AI has the potential to enhance the accuracy and efficiency of image interpretation. The research focuses on comparing different AI algorithms and approaches used for image analysis in radiography, aiming to identify the most effective methods for improving diagnostic outcomes. The introduction provides an overview of the significance of AI in radiography, highlighting the need for advanced image analysis techniques to support radiologists in making accurate and timely diagnoses. The background of the study discusses the evolution of AI in healthcare and its applications in radiography, emphasizing the potential benefits and challenges associated with its implementation. The problem statement identifies the gaps in current image analysis methods and the research questions that guide this study. The objectives of the study include evaluating the performance of various AI algorithms in image analysis, comparing their strengths and limitations, and determining the impact of AI on diagnostic accuracy and workflow efficiency in radiography. The limitations of the study are also addressed, acknowledging the constraints and potential biases that may influence the research outcomes. The scope of the study defines the specific focus areas and parameters of the comparative analysis, outlining the types of radiographic images and AI models considered. The significance of the study lies in its potential to enhance the quality of radiographic interpretations, reduce diagnostic errors, and improve patient outcomes through the integration of AI technologies. The structure of the thesis provides an overview of the chapters and their respective contents, guiding the reader through the research methodology, findings, and conclusions. The literature review explores existing studies and developments in AI-driven image analysis techniques in radiography, presenting a comprehensive overview of the current state-of-the-art approaches. The research methodology outlines the data collection methods, AI algorithms employed, and evaluation metrics used to compare the performance of different image analysis techniques. The discussion of findings presents the comparative analysis results, highlighting the strengths and weaknesses of each AI approach in radiographic image interpretation. The implications of these findings for clinical practice and future research are discussed, emphasizing the potential benefits of integrating AI into radiography workflows. In conclusion, this thesis provides valuable insights into the implementation of AI in radiography and its impact on automated image analysis. The study contributes to the growing body of knowledge on AI applications in healthcare and offers recommendations for improving diagnostic accuracy and workflow efficiency in radiology practice. Overall, the findings underscore the transformative potential of AI in revolutionizing radiographic image analysis and enhancing patient care.