Thesis Abstract

Abstract
This thesis explores the integration of Artificial Intelligence (AI) technology in radiography to enhance the accuracy of diagnostic procedures. The healthcare industry continues to evolve, with advancements in technology playing a pivotal role in improving patient outcomes. Radiography, as a critical component of diagnostic imaging, benefits immensely from the capabilities of AI systems. The primary objective of this research is to investigate the impact of AI applications on radiography practices and the subsequent enhancement of diagnosis accuracy. The study begins with an introduction to the increasing importance of AI in healthcare and the specific relevance of AI in radiography. A comprehensive review of the background of the study sheds light on the existing challenges in diagnostic accuracy and the potential of AI to address these challenges effectively. The problem statement highlights the limitations of traditional radiography methods and the necessity for more accurate diagnostic tools. The objectives of the study are outlined to provide a clear direction for the research, focusing on evaluating the efficacy of AI in improving diagnosis accuracy. The methodology chapter details the research design, data collection methods, and analysis techniques employed to investigate the impact of AI in radiography. A thorough literature review is conducted, covering ten key aspects related to AI applications in radiography, including machine learning algorithms, image processing techniques, and the integration of AI systems with radiology workflows. The research methodology chapter also discusses the selection criteria for study participants, data collection procedures, and the ethical considerations involved in the research process. The findings chapter presents a detailed analysis of the data collected, highlighting the benefits of incorporating AI in radiography for improved diagnosis accuracy. The discussion delves into the practical implications of AI technology in radiology departments, emphasizing the potential for enhanced efficiency and precision in diagnostic procedures. Various case studies and examples are examined to illustrate the real-world impact of AI in radiography settings. In conclusion, this thesis summarizes the key findings of the research, emphasizing the significant role of AI in improving diagnosis accuracy in radiography. The study underscores the importance of embracing technological advancements in healthcare to enhance patient care and outcomes. The implications of integrating AI systems in radiography are discussed, along with recommendations for future research and implementation strategies. Overall, this research contributes to the growing body of knowledge on the benefits of utilizing Artificial Intelligence in radiography for enhanced diagnostic accuracy and improved patient care.

Thesis Overview