Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements in recent years, with the integration of artificial intelligence (AI) emerging as a transformative technology. This thesis explores the application of AI in radiography to enhance diagnostic accuracy. The primary objective is to investigate how AI algorithms can be utilized to improve the interpretation of radiographic images, leading to more precise and timely diagnoses. The research begins with an in-depth analysis of the current landscape of radiography and the challenges faced by radiologists in interpreting complex imaging studies. A comprehensive review of the literature is conducted to understand the existing AI technologies used in radiography and their impact on diagnostic accuracy. The methodology chapter outlines the research design, data collection methods, and AI algorithms employed in the study. The research methodology involves the collection of radiographic images from various modalities and the application of AI algorithms for image analysis. The study also includes a comparative analysis of AI-assisted diagnosis with traditional radiological interpretation methods. The findings chapter presents the results of the study, including the accuracy rates of AI-assisted diagnoses compared to human interpretations. The discussion delves into the implications of these findings for the field of radiography and the potential benefits of integrating AI into clinical practice. The limitations of the study are also acknowledged, along with recommendations for future research in this area. In conclusion, this thesis highlights the significant potential of AI in radiography for improving diagnostic accuracy and patient outcomes. The integration of AI algorithms has the capacity to enhance the efficiency of radiological practices and provide valuable support to radiologists in their decision-making process. The findings of this study contribute to the growing body of literature on AI applications in radiography and underscore the importance of further research in this field to unlock the full benefits of this technology.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technology into the field of radiography to enhance diagnostic accuracy. Radiography plays a crucial role in healthcare by providing valuable insights into the internal structures of the human body through the use of medical imaging techniques. However, the interpretation of radiographic images can be complex and subjective, leading to potential errors in diagnosis. By leveraging the capabilities of AI, this research seeks to develop and implement advanced algorithms that can analyze radiographic images with a high level of precision and efficiency. AI technologies, such as machine learning and deep learning, have shown great potential in various medical applications, including radiology. These technologies can be trained to recognize patterns and abnormalities in radiographic images, thereby assisting radiologists in making more accurate and timely diagnoses. The research will involve a comprehensive review of existing literature on the use of AI in radiography and its impact on diagnostic accuracy. By examining previous studies and case reports, the project aims to identify the strengths and limitations of current AI systems in radiology and propose improvements for enhanced performance. Furthermore, the research methodology will involve the collection of radiographic images from various sources, including hospitals and medical imaging centers. These images will be used to train and test the AI algorithms, allowing for the evaluation of their diagnostic accuracy compared to traditional human interpretation. The findings of this research are expected to demonstrate the potential benefits of integrating AI technology into radiography practice. By improving diagnostic accuracy, AI-powered systems can help reduce the risk of misdiagnosis, enhance patient outcomes, and optimize healthcare resource utilization. Overall, this project seeks to contribute valuable insights into the application of artificial intelligence in radiography and its implications for improving diagnostic accuracy in medical imaging. The research findings are anticipated to pave the way for the widespread adoption of AI technologies in radiology practice, leading to more efficient and effective patient care."