Thesis Abstract

Abstract
The integration of artificial intelligence (AI) in the field of radiography has revolutionized the process of image analysis and diagnosis. This thesis explores the application of AI in radiography for enhancing accuracy, efficiency, and precision in medical imaging. The study delves into the background of AI technology in radiography, highlighting its potential to transform the healthcare industry. The main objective is to investigate how AI algorithms can be utilized to analyze radiographic images and aid in the diagnosis of various medical conditions. The research methodology involves a comprehensive review of existing literature on AI applications in radiography, focusing on ten key aspects that demonstrate the current state of the field. This literature review provides insights into the advancements, challenges, and future prospects of AI in radiography, laying the groundwork for the empirical investigation. The empirical study employs a mixed-methods approach, incorporating both quantitative and qualitative data analysis techniques. The research methodology includes eight components such as data collection, AI algorithm implementation, image analysis, diagnostic accuracy assessment, and evaluation of clinical outcomes. By conducting experiments and simulations, the study aims to evaluate the performance of AI systems in radiographic image analysis and diagnosis. The discussion of findings in Chapter Four presents a detailed analysis of the empirical results, including the accuracy rates, efficiency gains, and diagnostic outcomes achieved through the application of AI algorithms. The findings demonstrate the potential of AI to enhance the quality of radiographic imaging, improve diagnostic precision, and optimize clinical decision-making processes. In conclusion, this thesis underscores the significance of integrating AI technologies in radiography for image analysis and diagnosis. The study highlights the benefits of AI in enhancing healthcare outcomes, reducing diagnostic errors, and improving patient care. The findings contribute to the growing body of knowledge on AI applications in radiography and provide valuable insights for future research and development in the field.

Thesis Overview