Thesis Abstract

Abstract
This thesis investigates the use of Artificial Intelligence (AI) in radiography to enhance diagnostic accuracy in medical imaging. The integration of AI technologies in radiography has the potential to revolutionize the field by providing advanced tools for image analysis and interpretation. The study aims to explore the existing literature on AI applications in radiography, evaluate the benefits and challenges associated with AI implementation, and propose strategies for optimizing diagnostic accuracy through AI-based solutions. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms related to AI and radiography. The chapter lays the foundation for the subsequent chapters by establishing the context and rationale for investigating the use of AI in radiography. Chapter 2 comprises a comprehensive literature review that explores ten key areas related to AI in radiography. The review covers topics such as the history of AI in healthcare, current trends in AI applications for medical imaging, challenges in radiology practice, and the impact of AI on diagnostic accuracy. By synthesizing existing research findings, this chapter provides a theoretical framework for understanding the role of AI in improving diagnostic accuracy in radiography. Chapter 3 outlines the research methodology employed in the study, including the research design, data collection methods, sample selection, data analysis techniques, and ethical considerations. The chapter details the steps taken to investigate the use of AI in radiography and highlights the rigorous approach adopted to ensure the validity and reliability of the research findings. Chapter 4 presents a detailed discussion of the research findings, focusing on the impact of AI technologies on diagnostic accuracy in radiography. The chapter analyzes the results of the study, identifies patterns and trends in the data, and discusses the implications of the findings for radiography practice. By critically evaluating the effectiveness of AI tools in enhancing diagnostic accuracy, this chapter contributes to the existing body of knowledge on AI applications in radiography. Chapter 5 serves as the conclusion and summary of the thesis, providing a comprehensive overview of the key findings, implications, and recommendations arising from the study. The chapter reflects on the research objectives, discusses the significance of the findings for radiography practice, and suggests areas for future research and development in the field of AI-enhanced diagnostic imaging. Overall, this thesis contributes to the growing body of literature on the use of AI in radiography and offers valuable insights into how AI technologies can be leveraged to improve diagnostic accuracy in medical imaging. The findings of this study have important implications for healthcare professionals, researchers, and policymakers seeking to harness the potential of AI in radiography for enhanced patient care and clinical outcomes.

Thesis Overview

The research project titled "Investigating the Use of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technologies in radiography to enhance diagnostic accuracy. Radiography plays a crucial role in diagnosing various medical conditions by producing detailed images of the internal structures of the body. However, the interpretation of these images can sometimes be challenging, leading to potential errors in diagnosis. The introduction of AI in radiography has the potential to revolutionize the field by providing advanced image analysis capabilities and decision support tools for radiologists and other healthcare professionals. AI algorithms can analyze large volumes of imaging data quickly and accurately, helping to detect subtle abnormalities that may be missed by human observers. By leveraging machine learning and deep learning techniques, AI systems can continuously improve their diagnostic performance over time, leading to enhanced accuracy and efficiency in clinical practice. This research project will delve into the current landscape of AI applications in radiography, exploring the various AI technologies and algorithms being used in medical imaging analysis. By conducting a comprehensive literature review, the study will identify the strengths and limitations of existing AI systems in radiography and highlight the key challenges and opportunities in this rapidly evolving field. Furthermore, the research methodology will involve collecting and analyzing data from real-world radiology cases to evaluate the performance of AI algorithms in diagnosing common medical conditions. By comparing the diagnostic accuracy of AI systems with traditional radiological interpretations, this study aims to assess the potential benefits of AI integration in improving diagnostic outcomes and patient care. The findings of this research project will contribute valuable insights to the healthcare industry, demonstrating the effectiveness of AI technologies in enhancing diagnostic accuracy in radiography. By addressing the limitations and concerns surrounding AI implementation in clinical practice, this study will pave the way for the widespread adoption of AI-assisted radiological diagnosis, ultimately leading to improved patient outcomes and healthcare delivery. In conclusion, this research project will shed light on the transformative role of artificial intelligence in radiography and its potential to revolutionize diagnostic accuracy in medical imaging. By investigating the use of AI in radiography, this study aims to provide a comprehensive understanding of the benefits and challenges associated with integrating AI technologies into clinical practice, ultimately contributing to advancements in healthcare quality and patient care.