Thesis Abstract

Abstract
This thesis explores the integration of Artificial Intelligence (AI) in the field of radiography to enhance image analysis and diagnosis. The rapid advancements in AI technology offer promising opportunities to revolutionize the way medical imaging is interpreted and utilized for diagnostic purposes. The primary objective of this study is to investigate the potential benefits, challenges, and implications of incorporating AI algorithms in radiography practices. The research begins with an in-depth examination of the current landscape of radiography and the traditional methods employed in image analysis and diagnosis. This background provides a foundation for understanding the limitations and shortcomings of existing practices, highlighting the need for innovative solutions to improve efficiency and accuracy in radiological interpretations. Through a comprehensive literature review, ten key areas are identified that showcase the latest developments and applications of AI in radiography. These include image processing techniques, machine learning algorithms, deep learning models, computer-aided diagnosis systems, and AI-based decision support tools. The review synthesizes relevant studies and advancements in the field, shedding light on the potential impact of AI on radiography practices. The research methodology section outlines the approach taken to investigate the integration of AI in radiography. Detailed steps are provided for data collection, algorithm selection, model training, and evaluation methods. The study emphasizes the importance of rigorous testing and validation procedures to ensure the effectiveness and reliability of AI-driven diagnostic tools in real-world clinical settings. In the discussion of findings, the research outcomes are analyzed and interpreted to assess the performance of AI algorithms in enhancing image analysis and diagnosis in radiography. The results highlight the strengths and limitations of AI technologies, providing insights into their practical implications for radiologists, healthcare providers, and patients. In conclusion, this thesis summarizes the key findings, implications, and recommendations derived from the research on the application of AI in radiography. The study underscores the transformative potential of AI in improving the efficiency, accuracy, and quality of radiological interpretations, ultimately enhancing patient care and outcomes in the healthcare industry. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in radiography and provides valuable insights for researchers, practitioners, and decision-makers seeking to leverage advanced technologies for enhancing image analysis and diagnosis in medical imaging practices.

Thesis Overview