Thesis Abstract

Abstract
The field of radiography plays a crucial role in modern healthcare by providing essential diagnostic imaging services. However, the accuracy of radiographic interpretations can be influenced by various factors, including human error and variability. To address this challenge, the application of artificial intelligence (AI) has emerged as a promising solution to enhance diagnostic accuracy in radiography. This thesis investigates the effectiveness of AI in improving diagnostic accuracy in radiography and explores its potential impact on healthcare outcomes. The introduction sets the stage by highlighting the importance of accurate radiographic interpretations in healthcare and the potential benefits of integrating AI technology into radiography practices. The background of the study provides a comprehensive overview of the current state of radiography, emphasizing the need for improved diagnostic accuracy and the role of AI in achieving this goal. The problem statement identifies the existing challenges in radiographic interpretations, such as human error and variability, and underscores the significance of addressing these issues through AI solutions. The objectives of the study are outlined to guide the research process and evaluate the impact of AI on diagnostic accuracy in radiography. Limitations of the study are acknowledged, including potential constraints related to data availability, technology limitations, and other external factors that may influence the research outcomes. The scope of the study is defined to delineate the boundaries within which the research will be conducted and the specific aspects of AI application in radiography that will be explored. The significance of the study is highlighted, emphasizing the potential benefits of improving diagnostic accuracy through AI technology, such as enhanced patient care, reduced healthcare costs, and improved clinical outcomes. The structure of the thesis is outlined to provide a roadmap for the reader, detailing the organization of chapters and key sections within the research framework. Chapter two presents a comprehensive literature review that synthesizes existing knowledge on the application of AI in radiography and its impact on diagnostic accuracy. Ten key themes are identified and critically analyzed to provide a solid theoretical foundation for the research study. Chapter three details the research methodology, including study design, data collection methods, AI algorithms employed, and evaluation metrics used to assess diagnostic accuracy. Eight key components of the research methodology are elaborated upon to ensure transparency and rigor in the research process. Chapter four presents a detailed discussion of the research findings, highlighting the impact of AI on diagnostic accuracy in radiography and discussing the implications for healthcare practice. Key insights and trends are analyzed to provide a comprehensive understanding of the research outcomes. Chapter five offers a conclusion and summary of the thesis, summarizing the key findings, discussing the implications for future research and clinical practice, and highlighting the contributions of the study to the field of radiography and healthcare. The conclusion also reiterates the significance of AI in improving diagnostic accuracy and emphasizes the potential for transformative change in healthcare delivery. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI in radiography and its potential to enhance diagnostic accuracy and improve healthcare outcomes. By leveraging AI technology, radiography practices can achieve higher levels of precision and efficiency, ultimately benefiting patients, healthcare providers, and the broader healthcare system.

Thesis Overview