Thesis Abstract

Abstract
The utilization of Artificial Intelligence (AI) in the field of radiography has presented promising opportunities to enhance diagnostic accuracy and efficiency. This thesis investigates the application of AI technologies to improve diagnostic accuracy in radiography. The research explores the current landscape of AI integration in radiography, evaluates its impact on diagnostic processes, and identifies the challenges and limitations associated with its implementation. The study aims to provide valuable insights into how AI can be leveraged to optimize radiographic interpretations and ultimately improve patient outcomes. The introductory chapter sets the stage by presenting the background of the study, defining the problem statement, outlining the objectives, discussing the limitations and scope, highlighting the significance of the study, and providing an overview of the thesis structure. The literature review chapter critically examines existing research on AI in radiography, focusing on ten key aspects such as AI algorithms, image interpretation, workflow integration, and clinical outcomes. The research methodology chapter details the approach taken to investigate the application of AI in radiography. It includes discussions on research design, data collection methods, AI model development, validation techniques, and ethical considerations, among others. The chapter aims to provide a comprehensive understanding of the methodologies employed to address the research questions and achieve the study objectives. In the findings and discussion chapter, the research outcomes related to the application of AI in radiography are presented and analyzed in detail. The chapter delves into the key findings, interpretations, and implications of the study results, offering insights into the effectiveness of AI in improving diagnostic accuracy and its potential impact on clinical practice. Various case studies and examples are used to illustrate the practical implications of AI integration in radiography. The conclusion and summary chapter encapsulate the key findings of the study, reiterating the significance of AI in enhancing diagnostic accuracy in radiography. The chapter also discusses the implications of the research outcomes on future developments in the field and provides recommendations for further research and implementation strategies. Overall, this thesis contributes to the growing body of knowledge on the application of AI in radiography and its potential to revolutionize diagnostic practices for improved patient care. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Image Interpretation, Healthcare Technology, Machine Learning.

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 medical imaging for diagnosing various conditions and diseases, and the use of AI has the potential to revolutionize this process by providing advanced tools for image analysis and interpretation. The research will delve into the background of AI technology and its applications in healthcare, specifically in radiography. It will discuss the current challenges and limitations in traditional radiography practices, such as human error, subjective interpretation, and time-consuming image analysis. By incorporating AI algorithms and machine learning techniques, the project seeks to address these issues and improve the overall diagnostic accuracy of radiographic imaging. The study will focus on developing and implementing AI models that can assist radiographers and healthcare professionals in interpreting radiographic images more efficiently and accurately. This includes the automation of image segmentation, feature extraction, pattern recognition, and disease classification, ultimately leading to faster and more precise diagnoses. Furthermore, the research methodology will involve data collection from radiology departments, training AI models using large datasets of radiographic images, and evaluating the performance of the developed algorithms through comparative analysis with conventional diagnostic methods. The project will also consider ethical and regulatory considerations surrounding the use of AI in radiography, ensuring patient privacy, data security, and clinical validation of AI-assisted diagnoses. The findings of this research are expected to demonstrate the potential benefits of integrating AI technology into radiography practice, including improved diagnostic accuracy, reduced interpretation errors, enhanced workflow efficiency, and ultimately better patient outcomes. The discussion of findings will highlight the strengths and limitations of the AI models developed, as well as their implications for future research and clinical implementation. In conclusion, the project on the "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" represents a significant advancement in the field of radiology by leveraging AI technology to enhance the accuracy and effectiveness of diagnostic imaging. By harnessing the power of AI, radiographers and healthcare professionals can make more informed decisions, leading to better patient care and outcomes."