Thesis Abstract

Abstract
This thesis explores the application 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 improving the efficiency and effectiveness of diagnostic processes. The research investigates the current landscape of AI in radiography, identifies the challenges and limitations faced, and proposes innovative solutions to enhance diagnostic accuracy. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions of terms. Chapter Two comprises a comprehensive literature review that examines existing studies on the application of AI in radiography, discussing ten key areas such as AI algorithms, image processing techniques, and the role of AI in diagnostics. Chapter Three outlines the research methodology employed in this study, including the research design, data collection methods, data analysis techniques, and ethical considerations. The chapter also details the development and validation of AI models for diagnostic accuracy in radiography. In Chapter Four, the findings of the research are discussed in detail, highlighting the impact of AI on diagnostic accuracy in radiography. The chapter presents the results of experiments conducted to evaluate the performance of AI models and compares them with traditional diagnostic methods. The implications of these findings for clinical practice and future research are also discussed. Chapter Five concludes the thesis by summarizing the key findings and contributions of the research. The study highlights the potential of AI technologies to enhance diagnostic accuracy in radiography and offers recommendations for the integration of AI systems in clinical settings. Overall, this research contributes to the advancement of radiography practice by leveraging AI for improved diagnostic outcomes. In conclusion, the "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" thesis underscores the significant role of AI in revolutionizing the field of radiography. By harnessing the power of AI technologies, healthcare professionals can enhance diagnostic accuracy, improve patient outcomes, and advance the practice of radiography to new heights.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technology into radiography to enhance the accuracy of diagnostic procedures. Radiography plays a crucial role in medical imaging, providing valuable insights into various health conditions. However, traditional radiographic interpretations may be subject to human error, leading to potential misdiagnoses and delays in treatment. By incorporating AI algorithms and machine learning models into radiography, this project seeks to improve diagnostic accuracy and efficiency. AI has the potential to analyze medical images rapidly and accurately, assisting radiologists in identifying abnormalities, detecting patterns, and making informed decisions. Through the utilization of AI, radiographic interpretations can be enhanced, leading to more precise diagnoses and improved patient outcomes. The research will involve a comprehensive review of existing literature on the application of AI in radiography, highlighting the benefits and challenges associated with this technology. Various AI techniques, such as deep learning, convolutional neural networks, and image segmentation, will be explored to understand their potential in enhancing diagnostic accuracy in radiography. Furthermore, the project will outline a research methodology that includes data collection, image processing, AI model development, and validation using real-world radiographic datasets. The study will focus on evaluating the performance of AI models in comparison to traditional radiographic interpretations, assessing factors such as sensitivity, specificity, and overall diagnostic accuracy. The findings of this research aim to demonstrate the effectiveness of AI in improving diagnostic accuracy in radiography and its potential impact on clinical practice. By leveraging AI technology, healthcare facilities can streamline radiographic interpretations, reduce human error, and expedite the diagnosis and treatment of patients. The project seeks to contribute valuable insights to the field of radiography and pave the way for the widespread adoption of AI in medical imaging for enhanced patient care and outcomes.