Thesis Abstract

Abstract
The utilization of artificial intelligence (AI) in radiography has emerged as a promising approach to enhance diagnostic accuracy and improve patient outcomes. This thesis explores the integration of AI technologies in radiography to achieve improved diagnostic accuracy. The research investigates the potential benefits of AI in assisting radiographers and radiologists in interpreting medical images and making accurate diagnoses. Through a comprehensive literature review, the current landscape of AI applications in radiography is examined, highlighting the strengths and limitations of existing systems. The study outlines a research methodology that includes data collection, analysis, and evaluation of AI algorithms in radiography. Various AI techniques, such as deep learning, machine learning, and computer-aided diagnosis, are explored for their effectiveness in improving diagnostic accuracy. The research methodology also involves the development of a prototype AI system that can assist radiographers in interpreting complex medical images and detecting abnormalities with greater precision. The findings of this research demonstrate the potential of AI in radiography to enhance diagnostic accuracy and streamline the interpretation process. Through the analysis of real-world case studies and experimental data, the efficacy of AI algorithms in detecting and classifying abnormalities in medical images is evaluated. The results indicate that AI technologies can significantly improve diagnostic accuracy by providing radiographers with valuable insights and decision support tools. Furthermore, the study discusses the implications of integrating AI into radiography practice, including the challenges and ethical considerations associated with AI-driven diagnosis. The significance of this research lies in its contribution to advancing the field of radiography and improving patient care through the use of cutting-edge AI technologies. The study also highlights the importance of ongoing research and development in AI applications for radiography to enhance diagnostic accuracy and optimize healthcare delivery. In conclusion, the research underscores the potential of AI in radiography to revolutionize the field and improve diagnostic accuracy, ultimately leading to better patient outcomes. By harnessing the power of AI technologies, radiographers and radiologists can make more informed decisions, enhance diagnostic precision, and provide patients with timely and accurate diagnoses. This thesis serves as a comprehensive exploration of the utilization of artificial intelligence in radiography for improved diagnostic accuracy, paving the way for future advancements in this rapidly evolving field.

Thesis Overview

The project titled "Utilization of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" focuses on the integration of artificial intelligence (AI) technology into the field of radiography to enhance diagnostic accuracy. Radiography plays a crucial role in medical imaging for diagnosing various medical conditions, and the utilization of AI has the potential to revolutionize this process by providing advanced algorithms and automated tools for image analysis. The research aims to explore how AI can be effectively implemented in radiography to improve diagnostic accuracy, reduce errors, and enhance overall patient care. By leveraging AI capabilities such as machine learning and deep learning algorithms, radiographers and healthcare professionals can benefit from automated image interpretation, faster diagnosis, and more precise treatment planning. The project will delve into the background of AI in healthcare and radiography, highlighting the advancements and challenges in integrating AI technology into clinical practice. By conducting a comprehensive literature review, the research will explore existing studies, methodologies, and applications of AI in radiography to provide a solid foundation for the implementation of AI-based solutions. Furthermore, the research methodology will involve data collection, analysis, and the development of AI models tailored specifically for radiography applications. By collaborating with healthcare institutions and radiology departments, the project aims to gather real-world data and insights to train and validate AI algorithms for accurate image interpretation and diagnosis. The findings of the research will be discussed in detail, focusing on the effectiveness of AI in improving diagnostic accuracy in radiography. By analyzing the results and outcomes of the AI models developed, the research aims to demonstrate the potential benefits and limitations of AI integration in radiography practice. In conclusion, the project will provide valuable insights into the utilization of artificial intelligence in radiography for enhanced diagnostic accuracy. By harnessing the power of AI technology, radiographers and healthcare professionals can improve patient outcomes, streamline workflows, and elevate the standard of care in medical imaging.