Thesis Abstract

Artificial Intelligence (AI) has revolutionized various fields, and its application in radiography for image analysis and diagnosis holds immense potential for improving healthcare outcomes. This thesis explores the utilization of AI in radiographic image analysis and diagnosis, focusing on its impact, challenges, and future prospects. The research investigates how AI algorithms can enhance the accuracy and efficiency of radiographic interpretations, leading to timely and precise diagnoses. Chapter 1 introduces the research by providing a background of the study, outlining the problem statement, objectives, limitations, scope, significance, and defining key terms. The increasing demand for accurate and timely radiographic interpretations, coupled with the advancements in AI technology, sets the stage for exploring the application of AI in radiography. The research objectives aim to assess the effectiveness of AI algorithms in image analysis and diagnosis, identify limitations and challenges in implementation, and determine the scope and significance of integrating AI in radiography. Chapter 2 presents a comprehensive literature review encompassing ten key aspects related to the application of AI in radiographic image analysis and diagnosis. The review explores existing studies, methodologies, and technologies used in AI applications for radiography. It discusses the benefits of AI in improving diagnostic accuracy, reducing interpretation time, and enhancing patient care outcomes. Additionally, the review highlights challenges such as data privacy concerns, algorithm bias, and regulatory issues that need to be addressed for successful implementation of AI in radiography. Chapter 3 details the research methodology employed to investigate the application of AI in radiographic image analysis and diagnosis. The chapter delineates the research design, data collection methods, AI algorithms used, sample size, data analysis techniques, and ethical considerations. The methodology aims to provide a systematic approach to evaluating the performance of AI algorithms in radiographic interpretation and diagnosing various medical conditions. Chapter 4 presents a comprehensive discussion of the findings obtained from the research. The chapter analyzes the effectiveness of AI algorithms in radiographic image analysis, compares their performance with traditional methods, identifies areas of improvement, and discusses the implications for clinical practice. The discussion delves into the potential challenges faced in integrating AI into radiography, such as algorithm interpretability, data quality, and human-machine interaction. Chapter 5 concludes the thesis by summarizing the key findings, implications, and recommendations for future research and practice. The study underscores the transformative potential of AI in radiographic image analysis and diagnosis, emphasizing the need for continued research, collaboration, and innovation in leveraging AI technology to enhance healthcare delivery. The conclusion reflects on the significance of integrating AI into radiography to improve diagnostic accuracy, patient outcomes, and overall healthcare efficiency. Overall, this thesis contributes to the growing body of literature on the application of AI in radiographic image analysis and diagnosis. By exploring the challenges, opportunities, and implications of AI integration in radiography, this research seeks to advance knowledge and stimulate further inquiry into the transformative role of AI in healthcare

Thesis Overview