Implementation of Artificial Intelligence in Radiography for Image Analysis and Diagnosis

Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements in recent years, with the integration of artificial intelligence (AI) technologies offering promising opportunities for enhancing image analysis and diagnosis. This thesis explores the implementation of AI in radiography for image analysis and diagnosis, aiming to improve the accuracy and efficiency of radiological assessments. The study investigates the potential benefits and challenges associated with integrating AI into radiography practices, focusing on the development of AI algorithms for automated image interpretation and diagnosis. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. The literature review in Chapter 2 examines existing research on AI applications in radiography, highlighting key findings and gaps in current knowledge. Chapter 3 outlines the research methodology, detailing the research design, data collection methods, AI algorithm development, and evaluation criteria. Chapter 4 presents a comprehensive discussion of the research findings, including the performance evaluation of the developed AI algorithms and their impact on radiography practice. The results demonstrate the potential of AI to enhance image analysis accuracy, reduce interpretation time, and improve diagnostic outcomes. The discussion also addresses the challenges and limitations encountered during the research process. In Chapter 5, the thesis concludes with a summary of the key findings, implications for radiography practice, and recommendations for future research. The study underscores the transformative potential of AI in radiography for improving patient care outcomes, radiologist efficiency, and overall healthcare quality. The thesis contributes to the growing body of knowledge on AI applications in radiography and provides valuable insights for healthcare professionals, researchers, and policymakers seeking to leverage AI technologies for enhanced image analysis and diagnosis in radiology. Overall, this thesis offers a comprehensive analysis of the implementation of AI in radiography for image analysis and diagnosis, highlighting the opportunities and challenges involved in integrating AI technologies into radiological practice. The findings of this research have important implications for the future of radiography and underscore the transformative potential of AI in advancing healthcare delivery and patient outcomes.