Thesis Abstract

Abstract
Radiography plays a critical role in modern healthcare by providing essential diagnostic information for a wide range of medical conditions. However, the interpretation of radiographic images can be challenging and subjective, leading to variability in diagnostic accuracy. The integration of artificial intelligence (AI) technologies in radiography has the potential to address these challenges and improve diagnostic accuracy significantly. This thesis explores the implementation of AI in radiography to enhance diagnostic accuracy and patient outcomes. The introduction sets the stage by discussing the background of the study, the problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of terms. The literature review in Chapter Two provides a comprehensive overview of existing research on AI applications in radiography, highlighting the benefits and challenges associated with these technologies. The review covers topics such as machine learning algorithms, deep learning models, computer-aided diagnosis systems, and the impact of AI on radiographic interpretation. Chapter Three outlines the research methodology employed in this study, including data collection methods, AI model development, training and validation procedures, and evaluation metrics used to assess diagnostic accuracy. The methodology also addresses ethical considerations, data privacy concerns, and potential biases in AI algorithms. The discussion of findings in Chapter Four presents the results of the AI model evaluation, including comparisons with human radiologists, assessment of diagnostic accuracy improvements, and analysis of factors influencing model performance. The conclusion and summary in Chapter Five reflect on the key findings of the study, implications for clinical practice, limitations of the research, and recommendations for future research directions. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in radiography and its potential to enhance diagnostic accuracy, reduce variability in interpretations, and improve patient care outcomes. The findings underscore the importance of continuous research and collaboration between healthcare professionals and AI experts to optimize the implementation of AI technologies in radiographic practice.

Thesis Overview