Thesis Abstract

Abstract
This thesis investigates the application of artificial intelligence (AI) in enhancing the quality of diagnostic images in radiography. The use of AI in radiography has gained significant attention due to its potential to improve diagnostic accuracy, efficiency, and patient outcomes. The study aims to explore how AI technologies can be effectively integrated into radiography practice to enhance image quality and optimize diagnostic processes. The research begins with an introduction that provides background information on the use of AI in radiography and highlights the importance of improving diagnostic image quality. The problem statement identifies the challenges and limitations faced in traditional radiography practices, emphasizing the need for innovative solutions to enhance image interpretation and diagnosis accuracy. The objectives of the study are outlined to guide the research process and address the research questions comprehensively. The literature review chapter critically examines existing studies and research findings related to the use of AI in radiography and its impact on diagnostic image quality. Various AI techniques, such as machine learning, deep learning, and image processing algorithms, are discussed in detail to provide a comprehensive understanding of their applications in radiography. The review also explores the benefits, challenges, and limitations associated with the integration of AI technologies in radiography practice. The research methodology chapter outlines the research design, data collection methods, and analysis techniques employed in the study. The research approach includes a combination of qualitative and quantitative methods to gather data from radiography professionals, AI experts, and patients. The data analysis process involves the use of statistical tools and software to analyze and interpret the research findings effectively. The discussion of findings chapter presents the results of the study, highlighting the impact of AI technologies on diagnostic image quality in radiography. The findings reveal the potential benefits of using AI algorithms to enhance image interpretation, reduce diagnostic errors, and improve patient outcomes. The discussion also addresses the challenges and limitations of AI integration in radiography practice and provides recommendations for overcoming these obstacles. In conclusion, this thesis emphasizes the significance of integrating AI technologies in radiography to improve diagnostic image quality and enhance patient care. The study contributes to the existing body of knowledge by providing insights into the potential applications of AI in radiography and offering practical recommendations for implementing AI solutions in clinical practice. Overall, the research findings support the use of AI as a valuable tool for enhancing diagnostic image quality in radiography and advancing healthcare delivery.

Thesis Overview

The research project titled "Investigating the Use of Artificial Intelligence in Improving Diagnostic Image Quality in Radiography" aims to explore the potential benefits and challenges associated with integrating artificial intelligence (AI) technologies into radiography practice. As advancements in AI continue to revolutionize various industries, the healthcare sector, particularly radiology, stands to benefit significantly from the capabilities of AI in enhancing diagnostic accuracy and efficiency. The project will delve into the current landscape of radiography and the existing challenges faced by radiographers in interpreting diagnostic images accurately and efficiently. By leveraging AI technologies, such as machine learning algorithms and deep learning models, the research seeks to investigate how these tools can assist radiographers in improving the quality of diagnostic images and streamlining the diagnostic process. Key areas of focus in the research overview include an examination of the capabilities of AI in image processing, pattern recognition, and anomaly detection within radiography. By harnessing the power of AI, radiographers can potentially enhance their diagnostic capabilities, reduce errors, and expedite the delivery of accurate and timely diagnoses to patients. Moreover, the research overview will explore the ethical considerations and potential limitations associated with the integration of AI in radiography practice. Issues such as data privacy, algorithm bias, and the impact on radiographer-patient relationships will be critically analyzed to ensure the responsible and ethical implementation of AI technologies in healthcare settings. Overall, the research project aims to contribute valuable insights into the transformative potential of AI in radiography and its role in improving diagnostic image quality. By bridging the gap between AI technology and radiography practice, this study seeks to pave the way for more effective and efficient diagnostic processes that ultimately benefit both healthcare providers and patients.