Thesis Abstract

Abstract
The advancement of artificial intelligence (AI) technology has revolutionized various industries, including healthcare. This thesis investigates the application of AI in radiography to enhance diagnostic accuracy. The primary objective is to explore how AI tools can be integrated into radiography practices to improve the efficiency and effectiveness of diagnostic processes. The study reviews existing literature on the use of AI in radiography and identifies gaps and opportunities for further research. The research methodology chapter outlines the approach taken to collect and analyze data, including the use of case studies and interviews with radiographers and AI experts. Findings from these data collection methods are presented in chapter four, where the discussion focuses on the benefits and challenges of implementing AI in radiography. The study reveals that AI technologies, such as machine learning algorithms and image recognition systems, have the potential to significantly enhance diagnostic accuracy by assisting radiographers in interpreting medical images more efficiently. The findings also highlight the importance of proper training and collaboration between AI systems and human radiographers to ensure optimal outcomes. The conclusion chapter summarizes the key findings of the study and offers recommendations for future research and practical implementation of AI in radiography. Overall, this thesis contributes to the growing body of knowledge on the use of AI in healthcare and provides insights into how AI can be leveraged to improve diagnostic accuracy in radiography practices.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" focuses on leveraging artificial intelligence (AI) technology to enhance the accuracy and efficiency of diagnostic processes in the field of radiography. Radiography plays a crucial role in medical imaging for diagnosing various conditions and diseases. However, the interpretation of radiographic images can be challenging and subjective, leading to potential errors and inconsistencies in diagnosis. By incorporating AI algorithms and machine learning techniques into radiography, this research aims to improve diagnostic accuracy by providing radiologists and healthcare professionals with advanced tools for image analysis and interpretation. AI can assist in identifying patterns, anomalies, and potential abnormalities in radiographic images that may be overlooked by human observers. This can lead to earlier detection of diseases, more precise diagnosis, and improved patient outcomes. The research will involve a comprehensive review of existing literature on the application of AI in radiography, exploring the latest advancements, challenges, and opportunities in this rapidly evolving field. It will also include the development and implementation of AI models specifically tailored for radiographic image analysis, utilizing deep learning algorithms and image processing techniques to extract meaningful information from medical images. Furthermore, the project will outline a detailed research methodology involving data collection, preprocessing, model training, validation, and evaluation to assess the performance and efficacy of the AI system in improving diagnostic accuracy. Real-world radiographic datasets will be used to test the AI models and compare their results with traditional diagnostic methods. The discussion of findings will analyze the impact of AI integration in radiography, highlighting the strengths, limitations, and potential implications for clinical practice. The project will also address ethical considerations, data privacy concerns, and the need for regulatory frameworks to ensure the responsible deployment of AI technologies in healthcare settings. In conclusion, the "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" project aims to demonstrate the transformative potential of AI in revolutionizing radiographic imaging and diagnosis. By harnessing the power of AI technology, healthcare providers can enhance diagnostic accuracy, optimize workflow efficiency, and ultimately improve patient care in the field of radiography.