Thesis Abstract

Abstract
The advent of artificial intelligence (AI) has revolutionized various industries, including healthcare, by enhancing efficiency and accuracy in decision-making processes. In the field of radiography, AI has shown great promise in improving diagnostic accuracy and patient outcomes. This thesis explores the role of artificial intelligence in enhancing diagnostic accuracy in radiography, with a focus on its applications, benefits, challenges, and implications for the radiography profession. The introduction provides an overview of the significance of AI in healthcare and radiography, highlighting the need for improved diagnostic accuracy and the potential of AI to address this challenge. The background of the study delves into the evolution of AI in radiography and its impact on diagnostic practices. The problem statement identifies the gaps in current diagnostic processes and emphasizes the importance of integrating AI technologies to overcome these limitations. The objectives of the study include evaluating the effectiveness of AI in enhancing diagnostic accuracy, exploring the challenges and limitations associated with AI implementation in radiography, and assessing the implications of AI on the radiography profession. The scope of the study encompasses various AI applications in radiography, including image analysis, diagnosis assistance, and treatment planning. The literature review critically analyzes existing research on AI in radiography, focusing on key themes such as machine learning algorithms, deep learning models, and computer-aided diagnosis systems. The research methodology outlines the study design, data collection methods, and analysis techniques employed to investigate the role of AI in improving diagnostic accuracy. The discussion of findings presents the results of the study, highlighting the impact of AI on diagnostic accuracy, the challenges faced in implementing AI technologies, and the potential benefits for radiography practice. The conclusion summarizes the key findings of the thesis, emphasizing the importance of AI in enhancing diagnostic accuracy and improving patient outcomes in radiography. Overall, this thesis contributes to the growing body of knowledge on the role of artificial intelligence in radiography and provides valuable insights into the potential benefits and challenges associated with integrating AI technologies into diagnostic practices. The findings of this study have important implications for radiography professionals, healthcare institutions, and policymakers, highlighting the need for continued research and innovation in leveraging AI to improve diagnostic accuracy and patient care.

Thesis Overview

The research project titled "The Role of Artificial Intelligence in Improving Diagnostic Accuracy in Radiography" aims to investigate and evaluate the impact of artificial intelligence (AI) on enhancing the diagnostic accuracy in the field of radiography. This study is motivated by the increasing adoption of AI technologies in healthcare settings, particularly in medical imaging and radiology, where AI has demonstrated potential in assisting radiographers and physicians in interpreting complex images and improving diagnostic outcomes. The use of AI algorithms and machine learning techniques in radiography has shown promising results in automating image analysis, detecting abnormalities, and providing decision support to healthcare professionals. By leveraging AI technology, radiographers can potentially reduce interpretation errors, expedite diagnosis, and enhance overall patient care quality. This research will begin by exploring the background of AI in radiography, including the development of AI applications in medical imaging and the current state of AI adoption in radiology departments. The study will identify key challenges and limitations faced by radiographers in traditional diagnostic processes and how AI can address these challenges to improve diagnostic accuracy. The project will also define the specific objectives of the study, which include assessing the effectiveness of AI algorithms in detecting abnormalities in medical images, evaluating the impact of AI on radiographer performance and diagnostic accuracy, and identifying potential barriers to the implementation of AI technology in radiography practice. Furthermore, the research will outline the methodology employed, which will involve a systematic literature review of existing studies on AI in radiography, data collection from radiography departments using AI tools, and qualitative interviews with radiographers and healthcare professionals to gather insights on their experiences with AI technology. The findings of this study will be discussed in detail, highlighting the advantages and limitations of using AI in radiography, as well as the implications for clinical practice and patient outcomes. The project will conclude with a summary of key findings, recommendations for future research, and practical implications for integrating AI into radiography practice to enhance diagnostic accuracy and improve patient care. Overall, this research overview underscores the importance of exploring the role of AI in radiography and its potential to revolutionize diagnostic processes, optimize workflow efficiency, and ultimately enhance the quality of healthcare delivery in radiology departments.