The use of artificial intelligence in radiography for improved image analysis and diagnosis.

Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the integration of artificial intelligence (AI) showing great promise in improving image analysis and diagnosis. This thesis explores the use of AI in radiography to enhance the accuracy and efficiency of diagnostic processes. The study begins with an introduction to the topic, providing background information on the evolution of radiography and the role of AI in healthcare. The problem statement highlights the challenges faced in traditional radiography practices and the need for AI solutions. The objectives of the study are outlined, focusing on the development and implementation of AI tools for image analysis and diagnosis. Limitations and scope of the study are discussed to provide a clear understanding of the research boundaries. A comprehensive literature review is conducted in Chapter Two, exploring ten key studies and advancements in AI applications in radiography. The review covers topics such as machine learning algorithms, deep learning networks, and image recognition technologies used in radiological imaging. The research methodology in Chapter Three outlines the approach taken to develop and evaluate AI models for image analysis. Methodological components include data collection, preprocessing, model training, and evaluation metrics, among others. Chapter Four presents the findings of the study, detailing the performance of AI models in image analysis and diagnosis compared to traditional radiography methods. The discussion delves into the strengths and limitations of AI applications, highlighting the potential benefits of integrating AI into radiography practices. Factors influencing the adoption of AI in radiography are analyzed, along with challenges and future research directions. The conclusion in Chapter Five summarizes the key findings and contributions of the study, emphasizing the significance of AI in enhancing radiography practices. Overall, this thesis provides valuable insights into the use of artificial intelligence in radiography for improved image analysis and diagnosis. The findings contribute to the growing body of knowledge on AI applications in healthcare and highlight the potential for AI to revolutionize radiography practices. By leveraging AI technologies, healthcare professionals can make more accurate and timely diagnoses, ultimately improving patient outcomes and advancing the field of radiography.