Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) into the field of radiography has revolutionized diagnostic imaging practices, offering the potential to enhance accuracy, efficiency, and patient outcomes. This thesis explores the Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy. The study aims to investigate the impact of AI technologies on radiographic imaging interpretation and diagnosis. The introductory chapter provides a comprehensive overview of the research background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter two presents a detailed literature review that examines existing studies and developments in AI applications within radiography, highlighting the benefits and challenges associated with this technology. Chapter three outlines the research methodology employed in this study, including the research design, data collection methods, sample selection, AI algorithms used, and data analysis techniques. The chapter also discusses ethical considerations and limitations of the research process. Chapter four presents a thorough discussion of the findings obtained from the research, analyzing the impact of AI implementation on diagnostic accuracy in radiography. The results are examined in relation to current practices, highlighting the potential benefits of AI in improving diagnostic accuracy and reducing errors in radiographic interpretation. Finally, chapter five provides a comprehensive conclusion and summary of the project thesis, emphasizing the key findings, implications, and recommendations for future research and clinical practice. The study concludes that the implementation of AI in radiography holds great promise for enhancing diagnostic accuracy, streamlining workflow, and ultimately improving patient care outcomes. In conclusion, this thesis contributes to the understanding of how AI technologies can be effectively integrated into radiography to enhance diagnostic accuracy. The findings provide valuable insights for radiographers, healthcare professionals, and policymakers seeking to leverage AI for improved diagnostic practices in medical imaging.

Thesis Overview