Thesis Abstract

Abstract
The integration of artificial intelligence (AI) into radiography has revolutionized the field of medical imaging, offering new avenues for improved diagnosis and patient care. This thesis explores the application of AI in radiography to enhance diagnostic accuracy and efficiency. The study delves into the background of AI in radiography, highlighting its potential to transform traditional practices and improve healthcare outcomes. The research addresses the problem statement of current diagnostic challenges in radiography and aims to investigate how AI can mitigate these challenges. The objectives of the study include evaluating the effectiveness of AI algorithms in detecting abnormalities in medical images, assessing the impact of AI on radiography workflow and efficiency, and exploring the potential limitations and challenges of implementing AI in radiography practice. The scope of the study encompasses a comprehensive review of literature on AI applications in radiography, as well as an empirical investigation into the practical implications of AI integration in clinical settings. The significance of this research lies in its potential to enhance diagnostic accuracy, reduce interpretation errors, and improve patient outcomes in radiography. By leveraging AI technologies, radiographers and healthcare professionals can benefit from enhanced decision support tools and streamlined workflows, ultimately leading to more efficient and effective patient care. The structure of the thesis is organized into five main chapters. Chapter 1 provides an introduction to the research topic, background information on AI in radiography, a detailed problem statement, research objectives, limitations, scope, significance, and the overall structure of the thesis. Chapter 2 presents a comprehensive literature review on AI applications in radiography, covering key concepts, developments, and trends in the field. Chapter 3 outlines the research methodology, including the study design, data collection methods, sample selection, data analysis techniques, and ethical considerations. The chapter also describes the AI algorithms and technologies utilized in the study and their implications for radiography practice. In Chapter 4, the findings of the research are discussed in detail, focusing on the efficacy of AI algorithms in detecting abnormalities in medical images, the impact of AI on radiography workflow and efficiency, and the challenges and limitations of AI integration in clinical practice. The discussion is supported by empirical data and analysis from the study. Finally, Chapter 5 presents the conclusion and summary of the thesis, highlighting the key findings, implications for practice, recommendations for future research, and concluding remarks on the potential of AI in radiography for improved diagnosis. Overall, this thesis contributes to the growing body of knowledge on AI applications in radiography and underscores the transformative potential of AI technology in enhancing diagnostic accuracy and patient care.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnosis" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to enhance the accuracy and efficiency of diagnostic processes. The utilization of AI in radiography has the potential to revolutionize the way medical imaging is interpreted and analyzed, leading to improved patient outcomes and better decision-making by healthcare professionals. The research will delve into the background of AI in radiography, highlighting the evolution of AI technologies and their applications in the medical field. It will examine the current challenges faced in radiography, such as the time-consuming nature of image analysis and the potential for human error in interpretation. By incorporating AI algorithms into radiographic imaging, the project seeks to address these challenges and streamline the diagnostic workflow. The study will identify the specific problems within the field of radiography that can be mitigated through the implementation of AI, such as improving image quality, reducing interpretation time, and enhancing diagnostic accuracy. By defining clear objectives, the research aims to demonstrate how AI can be leveraged to achieve these goals and enhance overall diagnostic capabilities in radiography. Furthermore, the project will outline the limitations of integrating AI in radiography, including concerns related to data privacy, algorithm bias, and the need for continuous training and validation of AI models. By understanding these limitations, the research will provide insights into the ethical and practical considerations that must be addressed when implementing AI technologies in healthcare settings. The scope of the study will encompass a detailed examination of various AI techniques and algorithms that can be applied to radiographic imaging, such as machine learning, deep learning, and computer-aided detection systems. By evaluating the strengths and weaknesses of these approaches, the project aims to identify the most effective AI solutions for improving diagnostic accuracy in radiography. The significance of the research lies in its potential to advance the field of radiography and enhance patient care through the integration of AI technologies. By improving the speed and accuracy of diagnostic processes, AI can help healthcare providers make more informed decisions and provide better treatment outcomes for patients. The findings of the study are expected to contribute valuable insights to the healthcare industry and pave the way for further advancements in AI-assisted radiographic imaging. In conclusion, the project "Application of Artificial Intelligence in Radiography for Improved Diagnosis" represents a critical exploration of how AI technologies can be harnessed to enhance diagnostic capabilities in radiography. Through a comprehensive analysis of AI techniques, limitations, and ethical considerations, the research aims to provide a roadmap for the successful integration of AI in radiographic imaging practices, ultimately leading to improved patient care and outcomes in the healthcare sector.