Thesis Abstract

Abstract
This thesis explores the impact of artificial intelligence (AI) on radiographic image quality in the field of diagnostic radiography. As technology continues to advance, the integration of AI in healthcare has become increasingly prevalent, offering opportunities for enhancing diagnostic accuracy and improving patient care. The aim of this study is to investigate how AI technologies influence the quality of radiographic images and the overall diagnostic process in radiography. The research begins with a comprehensive review of the existing literature on AI applications in radiography, highlighting the benefits and challenges associated with its implementation. The study then delves into the research methodology, which includes data collection, analysis, and evaluation of AI algorithms in radiographic imaging. Findings from the research indicate that AI has the potential to significantly enhance radiographic image quality by reducing noise, improving contrast, and aiding in the detection of abnormalities. However, challenges such as algorithm bias, data privacy concerns, and the need for ongoing training and validation of AI systems were also identified. The discussion section provides a detailed analysis of the research findings, exploring the implications of AI on radiographic image quality from both a technical and ethical perspective. The study emphasizes the importance of proper training and oversight in the integration of AI technologies to ensure accurate and reliable diagnostic outcomes. In conclusion, this thesis underscores the significance of AI in advancing radiographic imaging quality and its potential to revolutionize the field of diagnostic radiography. Recommendations for future research include further exploration of AI applications in radiography, continued development of AI algorithms, and ongoing evaluation of its impact on patient care and diagnostic accuracy. Overall, this study contributes to the growing body of knowledge on the integration of AI in radiography and provides valuable insights into the implications of artificial intelligence on radiographic image quality and diagnostic processes in healthcare.

Thesis Overview

The project titled "The Impact of Artificial Intelligence on Radiographic Image Quality in Diagnostic Radiography" aims to investigate the influence of artificial intelligence (AI) on the quality of radiographic images in the field of diagnostic radiography. This research seeks to explore how AI technologies can enhance the accuracy, efficiency, and overall quality of radiographic images, ultimately improving diagnostic outcomes and patient care. The integration of AI in radiography has the potential to revolutionize the way medical images are acquired, processed, and interpreted. By leveraging machine learning algorithms and deep learning techniques, AI systems can assist radiographers in detecting abnormalities, identifying patterns, and making diagnostic decisions with greater speed and precision. This project will delve into the various AI applications in radiography, such as image enhancement, artifact reduction, and automated image analysis, to evaluate their impact on image quality and diagnostic accuracy. Furthermore, the research will address the challenges and limitations associated with the implementation of AI in radiography, including issues related to data privacy, algorithm bias, and human-machine interaction. By critically analyzing the current state of AI in diagnostic radiography, this study aims to provide insights into best practices for integrating AI technologies into clinical practice while ensuring patient safety and data security. Through a comprehensive review of existing literature, empirical studies, and case examples, this research overview will offer a detailed analysis of the potential benefits and risks of AI in radiography. By examining real-world applications and research findings, this project seeks to contribute to the growing body of knowledge on the use of AI in diagnostic radiography and provide valuable recommendations for future research and clinical practice in this rapidly evolving field.