Utilization of Artificial Intelligence in Radiography for Automated Diagnosis and Treatment Planning

Thesis Abstract

Abstract
The advent of Artificial Intelligence (AI) has revolutionized the field of radiography, offering new possibilities for automated diagnosis and treatment planning. This thesis explores the utilization of AI in radiography to enhance the efficiency and accuracy of diagnostic processes and treatment planning. The study delves into the background of AI in healthcare, focusing on its application in radiography. It addresses the problem of manual diagnosis limitations and proposes AI as a solution to streamline and improve radiographic practices. The objectives of the study include investigating the effectiveness of AI algorithms in radiography, assessing the impact on diagnostic accuracy, and evaluating the potential for automated treatment planning. Limitations of the study, such as data availability and algorithm complexity, are also discussed, along with the scope of the research which covers a range of AI applications in radiography. The significance of this study lies in its potential to transform radiographic practices by introducing AI technologies that can assist radiographers and clinicians in making more accurate and timely diagnoses. The structure of the thesis is outlined, highlighting the various chapters that cover the introduction, literature review, research methodology, discussion of findings, and conclusion. Additionally, key terms and concepts relevant to the study are defined to provide clarity and context for readers. The literature review chapter provides an in-depth analysis of existing research on AI in radiography, exploring ten key aspects including AI algorithms, applications in diagnostic imaging, and challenges in implementation. The research methodology chapter outlines the approach taken in this study, covering data collection methods, algorithm selection criteria, and evaluation metrics used to assess the performance of AI in radiography. The discussion of findings chapter presents detailed analysis and interpretation of results obtained from implementing AI algorithms in radiographic diagnosis and treatment planning scenarios. In conclusion, this thesis underscores the transformative potential of AI in radiography, demonstrating its ability to automate and enhance diagnostic processes while improving treatment planning efficiency. The findings of this study contribute to the growing body of research on AI applications in healthcare and highlight the need for further investigation into the integration of AI technologies in radiographic practices.Ultimately, the utilization of AI in radiography holds promise for improving patient outcomes and advancing the field of diagnostic imaging and treatment planning.