Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in the field of Radiography to enhance diagnostic accuracy. The integration of AI technologies in radiography has the potential to revolutionize the field by improving the speed and accuracy of diagnoses, ultimately leading to better patient outcomes. The research aims to investigate the effectiveness of AI algorithms in assisting radiographers in interpreting medical images and making accurate diagnoses. The introduction provides an overview of the research topic, highlighting the increasing importance of AI in healthcare and the potential benefits it offers to the field of radiography. The background of the study delves into the evolution of AI in radiography and the current state of the technology in clinical practice. The problem statement emphasizes the challenges faced by radiographers in interpreting complex medical images accurately and the potential of AI to address these challenges. The objectives of the study focus on evaluating the performance of AI algorithms in analyzing medical images, comparing their accuracy with traditional methods, and assessing the impact of AI on diagnostic outcomes. The limitations of the study are acknowledged, including the availability of data, the complexity of AI algorithms, and potential ethical considerations. The scope of the study outlines the specific areas of radiography that will be examined and the types of AI technologies that will be evaluated. The significance of the study lies in its potential to improve diagnostic accuracy, reduce errors, and enhance patient care in radiography. The structure of the thesis is detailed, providing a roadmap for the reader to navigate through the research findings. Definitions of key terms used throughout the thesis are provided to ensure clarity and understanding. The literature review synthesizes existing research on the use of AI in radiography, covering topics such as machine learning algorithms, deep learning techniques, and AI applications in medical imaging. The research methodology outlines the approach taken to collect and analyze data, including the selection of AI models, image datasets, and evaluation metrics. The discussion of findings presents the results of the study, comparing the performance of AI algorithms with traditional methods and assessing their impact on diagnostic accuracy. The conclusion summarizes the key findings of the research, highlighting the strengths and limitations of AI in radiography and proposing recommendations for future research in this area. In conclusion, this thesis contributes to the growing body of knowledge on the use of AI in radiography and its potential to improve diagnostic accuracy. By leveraging the power of AI technologies, radiographers can enhance their capabilities, leading to more accurate diagnoses and improved patient outcomes.

Thesis Overview

The project titled "Investigating the Use of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the potential benefits and challenges associated with integrating artificial intelligence (AI) technology into radiography practice. Radiography plays a crucial role in the diagnosis and treatment of various medical conditions, and the utilization of AI has the potential to enhance diagnostic accuracy, efficiency, and patient outcomes. The research will delve into the background of AI technology and its applications in healthcare, with a specific focus on radiography. By conducting a comprehensive literature review, the project will provide a detailed analysis of existing studies and developments in AI-driven radiography systems. This review will help identify gaps in current research and highlight opportunities for further exploration in this field. One of the key objectives of the study is to address the problem statement of how AI can be effectively utilized to improve diagnostic accuracy in radiography. By setting clear research objectives, the project aims to investigate the potential benefits and limitations of integrating AI algorithms into radiographic imaging processes. The study will also explore the scope of AI technologies in radiography and assess their impact on clinical decision-making and patient care. Through a detailed research methodology, the project will outline the steps involved in data collection, analysis, and interpretation. By utilizing both quantitative and qualitative research methods, the study will gather insights from radiography professionals, AI experts, and healthcare providers to gain a holistic understanding of the implications of AI technology in radiography practice. The discussion of findings chapter will present a detailed analysis of the data collected, highlighting the key outcomes, trends, and insights derived from the research. This chapter will also discuss the implications of the findings for clinical practice, education, and future research directions in the field of AI-driven radiography. In the conclusion and summary chapter, the project will provide a comprehensive overview of the research findings and their implications for the field of radiography. The study will also highlight the significance of the research in advancing the use of AI technology for improved diagnostic accuracy in radiography practice. Overall, this research overview underscores the importance of investigating the use of artificial intelligence in radiography and its potential to revolutionize diagnostic accuracy and patient care in the healthcare industry. By exploring the opportunities and challenges associated with AI integration in radiography, this project aims to contribute valuable insights to the field and pave the way for future advancements in healthcare technology.