Implementation of Artificial Intelligence in Radiography for Enhanced Diagnostic Accuracy

Thesis Abstract

Abstract
The field of radiography plays a crucial role in modern healthcare by providing essential diagnostic imaging services to patients. With the rapid advancements in technology, there is a growing interest in integrating artificial intelligence (AI) to enhance diagnostic accuracy in radiography. This thesis explores the implementation of AI in radiography with the aim of improving diagnostic outcomes and overall patient care. The research focuses on developing and evaluating AI algorithms that can assist radiographers in interpreting medical images more accurately and efficiently. Chapter 1 provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes definitions of key terms related to AI and radiography. Chapter 2 presents a comprehensive literature review encompassing ten key areas related to AI in radiography. This section examines existing research, technologies, and applications of AI in medical imaging, highlighting the benefits and challenges associated with integrating AI into radiography practices. Chapter 3 outlines the research methodology employed in this study, detailing the research design, data collection methods, AI algorithm development process, and evaluation criteria. Additionally, this chapter discusses the ethical considerations and limitations of the research methodology. Chapter 4 delves into the discussion of findings obtained from the implementation of AI in radiography. The chapter analyzes the performance of AI algorithms in enhancing diagnostic accuracy, comparing results with traditional radiography methods. Furthermore, it explores the practical implications and potential impact of integrating AI in radiography workflows. Chapter 5 offers a conclusion and summary of the project thesis, summarizing the key findings, implications, and contributions to the field of radiography. The chapter also discusses future research directions and recommendations for healthcare professionals and policymakers. Overall, this thesis contributes to the ongoing dialogue on the integration of AI in radiography for improved diagnostic accuracy and patient care. By leveraging AI technologies, radiographers can enhance their diagnostic capabilities, leading to more accurate and efficient healthcare services for patients.