Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in radiography has significantly transformed the field of medical imaging by enhancing diagnostic accuracy and efficiency. This thesis explores the utilization of AI in radiography and its impact on improving healthcare outcomes. The study begins by examining the background of AI in radiography, highlighting its evolution and potential benefits. The problem statement identifies the existing challenges in traditional radiography practices, such as human error and time-consuming processes, which AI aims to address. The objectives of the study focus on evaluating the effectiveness of AI in enhancing diagnostic accuracy and efficiency in radiography. The literature review delves into ten key areas, including the evolution of AI in healthcare, the application of AI in medical imaging, the benefits and challenges of AI integration in radiography, and the current trends in AI-assisted radiology. The research methodology outlines the process of data collection, analysis, and evaluation, utilizing both qualitative and quantitative research methods. Key components of the methodology include data collection techniques, sample selection criteria, data analysis tools, and ethical considerations. The discussion of findings presents a detailed analysis of the impact of AI on diagnostic accuracy and efficiency in radiography. The results reveal the significant improvements achieved through AI integration, including faster image processing, enhanced image quality, and accurate diagnosis. The discussion also addresses the limitations and challenges associated with AI implementation in radiography, such as data privacy concerns and the need for continuous training and updates. In conclusion, this thesis demonstrates the transformative potential of AI in radiography, emphasizing its role in enhancing diagnostic accuracy and efficiency in healthcare settings. The study highlights the importance of ongoing research and development in AI technologies to further optimize radiography practices. By leveraging AI tools and algorithms, healthcare professionals can improve patient outcomes, streamline workflows, and provide more accurate and timely diagnoses. This research contributes to the growing body of knowledge on the utilization of AI in radiography and underscores its significance in advancing medical imaging practices.

Thesis Overview

The research project titled "Utilization of Artificial Intelligence in Radiography: Enhancing Diagnostic Accuracy and Efficiency" focuses on exploring the integration of artificial intelligence (AI) in the field of radiography to improve the accuracy and efficiency of diagnostic processes. In recent years, AI has shown great potential in various industries, including healthcare, by revolutionizing traditional practices and enhancing decision-making processes. The aim of this study is to investigate the benefits and challenges of incorporating AI technologies in radiography and how they can be utilized to improve diagnostic outcomes. The project will begin with a comprehensive literature review to examine existing studies, developments, and applications of AI in radiography. This review will highlight the current trends, challenges, and opportunities in the field, providing a solid foundation for the research. Subsequently, the research methodology will be outlined, detailing the approach, data collection methods, and analysis techniques that will be employed to achieve the study objectives. Through the collection and analysis of data, the study will delve into the practical implementation of AI tools and algorithms in radiography practices. This will involve exploring how AI can assist radiographers in interpreting images, detecting abnormalities, and making accurate diagnoses. The research will also investigate the impact of AI on workflow efficiency, resource utilization, and patient outcomes within radiology departments. Furthermore, the study will assess the limitations and challenges associated with the adoption of AI in radiography, including issues related to data privacy, ethical considerations, and potential biases in AI algorithms. By addressing these challenges, the research aims to provide insights into how AI technologies can be effectively integrated into radiography practices while ensuring patient safety, data security, and regulatory compliance. Overall, the project seeks to contribute to the growing body of knowledge on the utilization of AI in radiography and its potential to enhance diagnostic accuracy and efficiency. By exploring the benefits and challenges of AI integration in radiology departments, the study aims to provide valuable recommendations for healthcare institutions, radiographers, and policymakers looking to leverage AI technologies for improved patient care and diagnostic outcomes.