Thesis Abstract

Abstract
Radiography is an essential tool in medical imaging for diagnosing and monitoring a wide range of health conditions. With the advancements in technology, artificial intelligence (AI) has emerged as a promising approach to enhance the accuracy and efficiency of radiography diagnosis. This thesis explores the implementation of AI in radiography to improve diagnosis accuracy. The study begins with an introduction to the background of the research, highlighting the significance and relevance of integrating AI into radiography practices. The problem statement identifies the existing challenges in traditional radiography diagnosis, such as human error, subjective interpretations, and time-consuming processes. The objective of the study is to investigate how AI can address these challenges and improve the accuracy of radiography diagnosis. The limitations and scope of the study are also outlined to provide a clear understanding of the research boundaries. A comprehensive literature review is conducted in Chapter Two to explore the current state of AI applications in radiography, including image recognition, pattern analysis, and diagnostic decision support systems. The review synthesizes findings from various studies to establish the theoretical framework for implementing AI in radiography. Chapter Three details the research methodology employed in this study, including data collection methods, AI algorithms utilized, and evaluation metrics. The methodology section discusses the steps taken to train and test AI models using radiography datasets, ensuring the robustness and reliability of the results. In Chapter Four, the findings of the study are presented and discussed in detail. The results showcase the effectiveness of AI in improving the accuracy of radiography diagnosis, reducing interpretation errors, and enhancing workflow efficiency. The discussion delves into the implications of these findings for radiography practice and the potential benefits of integrating AI into clinical settings. The thesis concludes with Chapter Five, summarizing the key findings and contributions of the research. The conclusion highlights the significance of implementing AI in radiography for improved diagnosis accuracy and patient outcomes. Recommendations for future research and practical implications for healthcare professionals are also provided. Overall, this thesis contributes to the growing body of knowledge on the integration of AI in radiography and its potential to revolutionize diagnostic practices. By leveraging AI technology, radiographers can enhance their diagnostic capabilities, improve patient care, and ultimately advance the field of medical imaging.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnosis Accuracy" aims to explore the integration of artificial intelligence (AI) technologies into radiography practices to enhance the accuracy and efficiency of medical diagnoses. Radiography plays a crucial role in medical imaging, providing essential information for diagnosing various health conditions. However, the interpretation of radiographic images can be challenging and may sometimes lead to errors or delays in diagnosis. By incorporating AI algorithms and machine learning techniques into radiography, this project seeks to address these challenges and improve diagnostic accuracy. AI has shown great potential in analyzing medical images, detecting abnormalities, and assisting radiologists in interpreting complex imaging data. The project will focus on developing and implementing AI tools that can assist radiographers and radiologists in making more accurate and timely diagnoses. The research will begin with a comprehensive review of existing literature on the application of AI in radiography, highlighting the current trends, challenges, and opportunities in this field. Subsequently, the project will delve into the methodology used to develop and validate AI models for analyzing radiographic images. This will involve collecting and preprocessing a large dataset of radiographic images, training AI algorithms, and evaluating their performance in terms of accuracy, sensitivity, and specificity. Furthermore, the project will analyze the impact of AI integration on radiography practices, including its potential benefits in improving diagnostic accuracy, reducing interpretation errors, and enhancing workflow efficiency. The research will also consider the ethical and legal implications of using AI in healthcare and address potential concerns regarding data privacy, patient consent, and algorithm transparency. Overall, the project "Implementation of Artificial Intelligence in Radiography for Improved Diagnosis Accuracy" aims to contribute to the ongoing efforts to leverage AI technologies for enhancing healthcare outcomes. By exploring the integration of AI in radiography, this research seeks to pave the way for more accurate and efficient diagnostic processes, ultimately benefiting patients, healthcare providers, and the broader healthcare system.