Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in radiography has revolutionized the field of medical imaging, offering the potential to enhance diagnostic accuracy and efficiency. This thesis explores the utilization of AI in radiography to improve diagnostic accuracy through the analysis of various imaging modalities. The research delves into the background of AI in radiography, the problem statement, objectives, limitations, scope, and significance of the study. Furthermore, the thesis presents a detailed literature review covering ten key aspects related to AI in radiography. The research methodology section outlines the approach taken to investigate the impact of AI on diagnostic accuracy, including data collection, analysis techniques, and ethical considerations. Chapter four provides an in-depth discussion of the findings, highlighting the effectiveness of AI algorithms in improving diagnostic accuracy across different imaging techniques. The results indicate that AI systems can assist radiologists in detecting abnormalities, reducing interpretation errors, and enhancing overall diagnostic outcomes. Various case studies and examples demonstrate the practical application of AI in radiography, showcasing its potential to transform the field and improve patient care. Finally, chapter five presents a comprehensive conclusion and summary of the thesis, emphasizing the key findings, implications, and recommendations for future research and clinical practice. The study underscores the importance of integrating AI into radiography to enhance diagnostic accuracy, streamline workflow, and ultimately improve patient outcomes. Overall, this thesis contributes to the growing body of knowledge on AI in radiography and its potential to revolutionize medical imaging practices.

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 the accuracy of diagnostic processes. Radiography plays a crucial role in healthcare by utilizing imaging techniques to diagnose and monitor various medical conditions. However, the interpretation of radiographic images is a complex and time-consuming task that can be prone to human error. Artificial intelligence has emerged as a powerful tool that can revolutionize the field of radiography by augmenting the capabilities of radiologists and improving the accuracy of diagnostic outcomes. AI algorithms can analyze radiographic images with speed and precision, helping to identify patterns, anomalies, and potential abnormalities that may not be easily detected by the human eye. By leveraging AI technology, radiographers can enhance their diagnostic accuracy, reduce interpretation time, and ultimately improve patient outcomes. This research project aims to explore the potential benefits of utilizing AI in radiography for improved diagnostic accuracy. The study will investigate the current state of AI technology in radiography, examine existing AI algorithms used for image analysis, and evaluate the impact of AI on diagnostic processes. Through a comprehensive literature review and empirical analysis, the project seeks to identify the strengths and limitations of AI in radiography and propose strategies for optimizing its integration into clinical practice. Key objectives of the research include: 1. Investigating the background and significance of integrating AI in radiography. 2. Identifying the challenges and opportunities associated with AI adoption in radiography. 3. Assessing the impact of AI on diagnostic accuracy and efficiency in radiographic imaging. 4. Exploring the ethical and legal considerations surrounding the use of AI in healthcare. 5. Developing recommendations for implementing AI technology in radiography practice. By addressing these objectives, the research aims to contribute valuable insights to the field of radiography and healthcare innovation. The findings of this study are expected to inform healthcare professionals, policymakers, and technology developers on the potential of AI to transform radiographic imaging and enhance diagnostic accuracy. Ultimately, the project seeks to advance the integration of AI in radiography practice, leading to improved patient care and outcomes in the healthcare industry.