Thesis Abstract

Abstract
In recent years, the healthcare industry has witnessed a rapid advancement in technology, particularly in the field of radiography. The integration of Artificial Intelligence (AI) has shown great potential in revolutionizing the way medical imaging is interpreted and diagnosed. This thesis focuses on the implementation of AI in radiography to enhance diagnostic accuracy and ultimately improve patient outcomes. The introduction provides an overview of the background of the study, highlighting the growing importance of AI in healthcare and its potential impact on radiography. The problem statement identifies the current challenges faced in traditional radiography practices, such as human error and variability in interpretations, which can lead to misdiagnosis and delayed treatment. The objectives of the study aim to investigate how AI can be effectively integrated into radiography processes to improve diagnostic accuracy, streamline workflow, and enhance overall efficiency. The limitations of the study are also discussed, acknowledging potential constraints such as data availability, technical limitations, and ethical considerations. The scope of the study outlines the specific areas within radiography where AI can be applied, including image analysis, pattern recognition, and decision support systems. The significance of the study lies in its potential to transform radiography practices, leading to more accurate and timely diagnoses, improved patient care, and cost-effective healthcare delivery. The literature review chapter delves into existing research and developments in AI applications in radiography, covering topics such as machine learning algorithms, deep learning models, and computer-aided diagnosis systems. The chapter provides a comprehensive analysis of the current state of AI in radiography and identifies gaps in the literature that this study aims to address. The research methodology chapter details the approach taken to implement AI in radiography, including data collection methods, model development, validation techniques, and performance evaluation metrics. The chapter also discusses ethical considerations, data privacy issues, and potential biases in AI algorithms. The discussion of findings chapter presents the results of the study, showcasing how AI has improved diagnostic accuracy in radiography through case studies, comparative analyses, and performance metrics. The chapter also highlights the challenges encountered during the implementation process and offers recommendations for future research and practice. In conclusion, this thesis demonstrates the potential of AI in radiography to enhance diagnostic accuracy, improve patient outcomes, and streamline healthcare delivery. By leveraging the power of AI technologies, radiography practitioners can make more informed decisions, reduce diagnostic errors, and ultimately provide better care for patients. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Machine Learning, Deep Learning, Healthcare, Image Analysis, Decision Support Systems.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to enhance diagnostic accuracy. Radiography is a crucial medical imaging technique used for diagnosing various health conditions, and the advancement of AI presents an opportunity to streamline and improve this process. By leveraging AI algorithms and machine learning capabilities, radiographers can potentially achieve more accurate and efficient interpretations of medical images, leading to better patient outcomes. The research will begin with a comprehensive introduction, providing background information on radiography, the current challenges faced in diagnostic accuracy, and the potential benefits of integrating AI technology. The problem statement will highlight the limitations of traditional radiography methods and the need for innovative solutions to enhance diagnostic accuracy. The objectives of the study will be clearly defined, focusing on the implementation of AI algorithms to improve the interpretation of radiographic images. The study will also outline the scope and limitations of the research, detailing the specific aspects of radiography that will be examined and any constraints that may impact the findings. The significance of the study will be emphasized, highlighting the potential impact of implementing AI in radiography on patient care, healthcare efficiency, and overall diagnostic accuracy. The research methodology will be detailed, outlining the approach to data collection, analysis, and evaluation of AI algorithms in radiography. Various machine learning techniques and AI models will be explored to determine their effectiveness in enhancing diagnostic accuracy. The chapter on literature review will provide a comprehensive overview of existing research and developments in the field of AI in radiography, highlighting key findings and gaps in current knowledge. The discussion of findings chapter will present the results of the study, analyzing the effectiveness of AI algorithms in improving diagnostic accuracy in radiography. The conclusion and summary chapter will provide a comprehensive overview of the research findings, discussing the implications for clinical practice, future research directions, and potential challenges in implementing AI technology in radiography. Overall, the project aims to contribute to the advancement of radiography practice by exploring the potential benefits of integrating AI technology for improved diagnostic accuracy. By leveraging the capabilities of AI algorithms, radiographers can enhance their ability to interpret medical images accurately and efficiently, ultimately leading to better patient care and outcomes.