Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the integration of artificial intelligence (AI) emerging as a promising tool for enhancing diagnostic accuracy. This thesis explores the application of AI in radiography to improve the accuracy of diagnostic processes. The primary objective of this study is to investigate the effectiveness of AI technologies in assisting radiographers and healthcare professionals in interpreting medical images more accurately and efficiently. The thesis begins with an introduction that provides an overview of the research topic, followed by a background study that highlights the evolution of AI in radiography. The problem statement focuses on the challenges faced in traditional diagnostic processes and the potential benefits of integrating AI technologies. The objectives of the study are outlined to guide the research towards achieving specific goals, while also acknowledging the limitations and scope of the study. Chapter two presents a comprehensive literature review that examines existing studies and research on the use of AI in radiography. The review covers various aspects such as the types of AI technologies used, their impact on diagnostic accuracy, and the challenges associated with their implementation. By analyzing a range of scholarly articles, journals, and textbooks, this chapter aims to provide a thorough understanding of the current state of AI in radiography. Chapter three details the research methodology employed in this study, including the research design, data collection methods, and data analysis techniques. The methodology is designed to investigate the effectiveness of AI technologies in improving diagnostic accuracy by comparing traditional diagnostic methods with AI-assisted approaches. The chapter also discusses ethical considerations and potential biases that may influence the research outcomes. Chapter four presents a detailed discussion of the findings obtained from the research, including the analysis of data collected during the study. The findings highlight the impact of AI technologies on diagnostic accuracy, the challenges encountered in implementing AI systems, and the perspectives of radiographers and healthcare professionals on using AI in their practice. This chapter aims to provide insights into the practical implications of integrating AI in radiography and its potential benefits for patient care. Finally, chapter five offers a conclusion and summary of the thesis, consolidating the key findings and insights obtained from the research. The conclusion discusses the implications of the study for the field of radiography, highlighting the opportunities and challenges associated with adopting AI technologies. Recommendations for future research and potential areas for further exploration are also provided to contribute to the ongoing development of AI in radiography. In conclusion, this thesis contributes to the growing body of research on the use of artificial intelligence in radiography for improved diagnostic accuracy. By exploring the potential benefits and challenges of integrating AI technologies into radiographic practice, this study aims to enhance the quality of healthcare services and improve patient outcomes.

Thesis Overview

The research project titled "The Use of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to investigate the integration of artificial intelligence (AI) technology into the field of radiography to enhance the accuracy of diagnostic procedures. With the rapid advancements in AI technology, there is a growing interest in utilizing machine learning algorithms and deep learning models to assist radiographers in interpreting medical images more effectively. This research will delve into the current challenges faced in the field of radiography, such as human error, subjective interpretations, and time-consuming processes. By incorporating AI algorithms into radiography practices, this study seeks to address these challenges and improve the overall diagnostic accuracy of radiological images. The research will include a comprehensive literature review that examines previous studies and developments in the application of AI in radiography. This review will provide insights into the existing AI technologies, their capabilities, and their potential impact on diagnostic accuracy in radiology. Furthermore, the research methodology will outline the process of implementing AI algorithms in radiography, including data collection, training models, and evaluating the performance of AI systems in comparison to traditional diagnostic methods. The methodology will also address ethical considerations, data privacy issues, and potential limitations of AI technology in radiography. The findings of this research project are expected to demonstrate the effectiveness of AI in improving diagnostic accuracy in radiography. By comparing the results of AI-assisted diagnoses with those of human radiographers, this study aims to quantify the benefits of AI technology in reducing errors and enhancing the quality of patient care. In conclusion, this research project will contribute to the ongoing efforts to integrate AI technology into healthcare practices, particularly in the field of radiography. By harnessing the power of artificial intelligence, radiographers can achieve higher levels of diagnostic accuracy, leading to improved patient outcomes and more efficient healthcare delivery.