Utilization of Artificial Intelligence in Radiography for Automated Detection and Diagnosis of Pathologies

Thesis Abstract

Abstract
This thesis explores the utilization of Artificial Intelligence (AI) in radiography for automated detection and diagnosis of pathologies. Radiography is a vital medical imaging technique used for diagnosing various health conditions, and the integration of AI has the potential to enhance the accuracy and efficiency of radiographic interpretations. The primary objective of this research is to investigate the impact of AI technologies on the field of radiography and to evaluate its effectiveness in automating the detection and diagnosis of pathologies. The study begins with an introduction to the research topic, providing a background of the significance of AI in radiography and highlighting the current challenges in manual radiographic interpretation. The problem statement identifies the limitations of traditional radiographic practices and emphasizes the need for advanced technologies like AI to improve diagnostic accuracy and speed. The objectives of the study include assessing the performance of AI algorithms in detecting and diagnosing pathologies, identifying the limitations of current AI applications in radiography, and proposing recommendations for future research and implementation. A comprehensive review of the existing literature on AI in radiography is presented in Chapter Two. This literature review examines ten key studies and discusses the advancements, challenges, and potential applications of AI in radiographic imaging. Chapter Three outlines the research methodology, detailing the study design, data collection methods, participant selection criteria, and the AI algorithms used for automated detection and diagnosis of pathologies. The chapter also discusses the ethical considerations and limitations of the research methodology. Chapter Four presents a detailed discussion of the research findings, including the performance evaluation of AI algorithms in detecting and diagnosing various pathologies in radiographic images. The analysis includes the comparison of AI-assisted diagnoses with traditional manual interpretations, highlighting the strengths and limitations of AI technology in radiography. The chapter also explores the implications of AI integration for radiographers, healthcare providers, and patients. In Chapter Five, the conclusion and summary of the project thesis are provided, summarizing the key findings, implications, and recommendations for future research and implementation. The study confirms that AI technologies have the potential to significantly improve the accuracy and efficiency of radiographic interpretations, leading to more timely and precise diagnoses of pathologies. The significance of this research lies in its contribution to the advancement of AI applications in radiography and its potential to enhance healthcare outcomes. Overall, this thesis contributes to the growing body of research on the integration of AI in radiography and provides valuable insights into the benefits and challenges of using AI for automated detection and diagnosis of pathologies. The findings of this study have important implications for the future development and implementation of AI technologies in radiographic imaging, with the potential to revolutionize diagnostic practices and improve patient care in the healthcare industry.