Thesis Abstract

Abstract
This thesis presents a comprehensive study on the implementation of Artificial Intelligence (AI) in Radiography with a focus on comparing its diagnostic accuracy to traditional methods. The integration of AI technologies in healthcare has shown promising results in improving diagnostic accuracy, efficiency, and patient outcomes. Radiography, as a critical component of medical imaging, stands to benefit significantly from the advancements in AI. This research aims to investigate the effectiveness of AI algorithms in enhancing diagnostic accuracy in radiography and compare it with conventional diagnostic methods. The study begins with an introduction that highlights the background of the research, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review in Chapter Two provides a comprehensive analysis of existing studies, theories, and technologies related to AI in radiography. This chapter examines the evolution of AI in healthcare, the applications of AI in radiography, and the impact of AI on diagnostic accuracy. Chapter Three details the research methodology employed in this study. It includes the research design, data collection methods, AI algorithm selection criteria, sample population, data analysis techniques, and ethical considerations. The methodology aims to ensure the validity and reliability of the study results. In Chapter Four, the findings of the research are presented and discussed in detail. The comparative analysis between AI-assisted diagnosis and traditional radiographic interpretation provides insight into the diagnostic accuracy, efficiency, and reliability of AI systems. The discussion also explores the challenges, limitations, and potential implications of integrating AI in radiography practices. Lastly, Chapter Five concludes the thesis by summarizing the key findings, implications, and recommendations for future research and clinical practice. The conclusion emphasizes the importance of leveraging AI technologies to augment radiographic diagnostic accuracy and improve patient care outcomes. The study contributes to the growing body of knowledge on AI applications in healthcare and provides valuable insights for radiography practitioners, researchers, and policymakers. Overall, this thesis underscores the significance of implementing AI in radiography and highlights its potential to revolutionize diagnostic accuracy in medical imaging. The comparative study conducted in this research sheds light on the benefits and challenges of adopting AI technologies in radiography, paving the way for further advancements in healthcare diagnostics and patient care.

Thesis Overview

The research project titled "Implementation of Artificial Intelligence in Radiography: A Comparative Study on Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) in the field of radiography to enhance diagnostic accuracy. This project seeks to investigate how AI technologies can be effectively implemented in radiography practices to improve the precision and efficiency of diagnostic procedures, ultimately leading to better patient outcomes. The use of AI in radiography has gained significant attention in recent years due to its potential to revolutionize the way medical imaging is interpreted and analyzed. By leveraging AI algorithms and machine learning techniques, radiologists and healthcare professionals can benefit from advanced tools that assist in the detection, characterization, and classification of various medical conditions based on imaging data. This comparative study will involve examining the performance of AI-based diagnostic tools in comparison to traditional radiographic interpretation methods. By analyzing a set of pre-defined radiographic images and clinical data, the project aims to evaluate the accuracy, sensitivity, specificity, and overall diagnostic efficacy of AI systems in detecting and diagnosing specific medical conditions. Furthermore, this research will investigate the challenges and limitations associated with the implementation of AI in radiography, including issues related to data privacy, algorithm transparency, and integration with existing clinical workflows. By addressing these obstacles, the project aims to provide insights into the optimal utilization of AI technologies in radiography settings while ensuring patient safety and data security. The findings of this study are expected to contribute to the growing body of knowledge on the application of AI in radiography and its impact on diagnostic accuracy. By identifying the strengths and weaknesses of AI-driven diagnostic solutions, this research aims to provide recommendations for healthcare institutions, radiology departments, and technology developers seeking to enhance the quality and efficiency of radiographic imaging services through the integration of artificial intelligence. In conclusion, the project "Implementation of Artificial Intelligence in Radiography: A Comparative Study on Diagnostic Accuracy" represents a critical investigation into the potential benefits and challenges of incorporating AI technologies into radiographic practices. Through a comprehensive comparative analysis, this research endeavors to shed light on the evolving role of AI in radiography and its implications for improving diagnostic accuracy in clinical settings.