Thesis Abstract

Abstract
The integration of artificial intelligence (AI) into the field of radiography has revolutionized the diagnostic process, offering the potential for improved accuracy and efficiency in medical imaging interpretation. This thesis investigates the utilization of AI in radiography to enhance diagnostic accuracy, with a focus on its impact on clinical practice. The study aims to explore the benefits and challenges associated with AI implementation in radiography, as well as the potential implications for radiographers and healthcare providers. Through a comprehensive literature review, various AI applications in radiography, such as image analysis, pattern recognition, and decision support systems, are examined to assess their effectiveness in improving diagnostic outcomes. The research methodology employed in this study includes a mixed-methods approach, combining quantitative data analysis and qualitative investigation through interviews and surveys with radiography professionals. The study identifies key factors influencing the adoption of AI in radiography, including technological capabilities, regulatory considerations, and ethical concerns. By analyzing the current state of AI integration in radiography, the research aims to provide insights into the challenges and opportunities for enhancing diagnostic accuracy through AI-assisted imaging interpretation. The findings of this study reveal the potential of AI to improve diagnostic accuracy in radiography by assisting radiographers in image analysis and decision-making processes. The results highlight the importance of training and education for radiographers to effectively utilize AI tools and interpret AI-generated insights in clinical practice. Furthermore, the study identifies the need for ongoing research and development to address the limitations and maximize the benefits of AI in radiography. The conclusion of the thesis emphasizes the significance of AI integration in radiography for enhancing diagnostic accuracy and improving patient outcomes. The study underscores the importance of collaboration between radiographers, healthcare providers, and AI developers to ensure the successful implementation of AI technologies in clinical practice. The implications of this research contribute to the advancement of AI-assisted imaging interpretation in radiography and provide valuable insights for future research and practice in the field. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Medical Imaging, Image Analysis, Decision Support Systems, Healthcare Technology.

Thesis Overview