Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) in radiography to enhance diagnostic accuracy in medical imaging. The integration of AI technologies into radiographic practices has the potential to revolutionize the field by improving the efficiency and effectiveness of diagnostic processes. The study begins with an overview of the current landscape of radiography and the challenges faced in achieving accurate diagnoses. It then delves into the theoretical framework underpinning the use of AI in radiography, highlighting the various AI techniques and algorithms that can be applied to enhance diagnostic accuracy. A comprehensive review of relevant literature is conducted to examine the existing research and developments in AI applications in radiography. The literature review covers topics such as machine learning, deep learning, image recognition, and computer-aided diagnosis systems. The findings from this review inform the subsequent research methodology employed in this study. The research methodology section outlines the approach taken to investigate the implementation of AI in radiography for improved diagnostic accuracy. The methodology includes data collection methods, sample selection criteria, AI model development, validation processes, and performance evaluation metrics. The study utilizes a combination of quantitative and qualitative research methods to analyze the impact of AI technologies on diagnostic accuracy. The results of the study are presented in the discussion of findings chapter, which provides an in-depth analysis of the effectiveness of AI in improving diagnostic accuracy in radiography. The findings reveal the advantages and limitations of AI technologies in enhancing radiographic practices and highlight the potential for future advancements in the field. The implications of these findings are discussed in the context of the broader healthcare industry, emphasizing the importance of integrating AI into radiographic workflows. In conclusion, this thesis demonstrates the significant potential of AI in radiography for improving diagnostic accuracy and enhancing patient outcomes. The study contributes to the growing body of research on the application of AI technologies in healthcare and provides valuable insights for practitioners, researchers, and policymakers. The findings of this study underscore the importance of continued innovation and collaboration in leveraging AI to advance diagnostic practices in radiography. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Machine Learning, Deep Learning, Healthcare, Imaging, AI Technologies.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" focuses on the integration of artificial intelligence (AI) technology into the field of radiography to enhance diagnostic accuracy. Radiography is a crucial medical imaging technique used for diagnosing various conditions, including injuries, diseases, and abnormalities within the body. However, the interpretation of radiographic images can be complex and subjective, leading to potential errors and inconsistencies in diagnosis. The introduction of AI in radiography presents a promising solution to address these challenges by leveraging machine learning algorithms to assist radiologists in interpreting images more accurately and efficiently. AI algorithms can analyze large datasets of radiographic images, identify patterns, and provide insights that may not be easily discernible to the human eye. This technology has the potential to improve diagnostic accuracy, reduce interpretation errors, and enhance patient outcomes. The research will delve into the background of the study, highlighting the evolution of AI technology in healthcare and its applications in radiography. The project will identify the current challenges faced in radiographic image interpretation, such as variability in diagnoses, time constraints, and the growing demand for imaging services. By addressing these challenges, the implementation of AI in radiography aims to streamline the diagnostic process, facilitate faster decision-making, and ultimately improve patient care. The research methodology will involve a comprehensive literature review to examine existing studies, technologies, and applications related to AI in radiography. By analyzing the findings from previous research, the project aims to identify best practices, challenges, and opportunities for implementing AI in clinical practice. The methodology will also include data collection, analysis, and validation processes to evaluate the impact of AI on diagnostic accuracy and clinical outcomes. The discussion of findings will present the results of the research, highlighting the effectiveness of AI algorithms in improving diagnostic accuracy in radiography. The project will explore case studies, comparative analyses, and real-world applications of AI technology in radiology departments to demonstrate its practical benefits and limitations. By examining the findings in detail, the research aims to provide insights into the potential of AI to revolutionize radiographic imaging and enhance patient care. In conclusion, the project will summarize the key findings, implications, and recommendations for implementing AI in radiography for improved diagnostic accuracy. The research aims to contribute to the growing body of knowledge on the integration of AI technology in healthcare and its impact on radiology practice. By embracing AI as a valuable tool in radiography, healthcare providers can enhance diagnostic accuracy, optimize workflow efficiency, and ultimately improve patient outcomes.