Thesis Abstract

Abstract
Artificial Intelligence (AI) has shown promising potential in various fields, including healthcare, to enhance diagnostic accuracy and improve patient outcomes. This thesis investigates the use of AI in radiography to improve diagnostic accuracy, particularly in the detection and classification of abnormalities in medical images. The rapid advancements in AI technologies, such as deep learning algorithms and convolutional neural networks, have enabled automated analysis of radiographic images with high precision and efficiency. Chapter 1 Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Thesis 1.9 Definition of Terms Chapter 2 Literature Review 2.1 Overview of Radiography and Diagnostic Imaging 2.2 Evolution of Artificial Intelligence in Healthcare 2.3 Application of AI in Radiography 2.4 AI Algorithms for Image Analysis 2.5 Benefits of AI in Diagnostic Accuracy 2.6 Challenges and Limitations of AI in Radiography 2.7 Current Research and Development in AI for Radiography 2.8 Integration of AI with Radiology Practices 2.9 Ethical and Legal Considerations 2.10 Future Trends in AI for Radiography Chapter 3 Research Methodology 3.1 Research Design 3.2 Data Collection Methods 3.3 Image Dataset Preparation 3.4 Selection of AI Models 3.5 Model Training and Validation 3.6 Performance Evaluation Metrics 3.7 Ethical Approval and Data Privacy 3.8 Statistical Analysis Chapter 4 Discussion of Findings 4.1 Analysis of AI Model Performance 4.2 Comparison with Conventional Diagnostic Methods 4.3 Interpretation of Results 4.4 Clinical Relevance and Impact on Patient Care 4.5 Addressing Limitations and Challenges 4.6 Suggestions for Future Research 4.7 Implications for Radiography Practice Chapter 5 Conclusion and Summary In conclusion, this thesis explores the use of AI in radiography to enhance diagnostic accuracy and improve patient care. The findings suggest that AI technologies have the potential to revolutionize radiology practices by providing automated and accurate analysis of medical images. By leveraging AI algorithms for image interpretation, radiologists can expedite the diagnostic process, reduce errors, and enhance overall efficiency. However, the implementation of AI in radiography requires careful consideration of ethical, legal, and practical implications to ensure patient safety and data security. Future research should focus on optimizing AI models for specific radiographic applications and integrating them seamlessly into clinical workflows to realize the full potential of AI in improving diagnostic accuracy in radiography.

Thesis Overview

The project titled "Investigating the Use of Artificial Intelligence in Improving Diagnostic Accuracy in Radiography" aims to explore the potential benefits and challenges associated with integrating artificial intelligence (AI) technologies in the field of radiography. Radiography plays a crucial role in medical imaging by providing detailed images of internal structures to assist in diagnosis and treatment planning. However, the interpretation of radiographic images can be complex and time-consuming, requiring a high level of expertise from radiologists. The introduction of AI in radiography has the potential to revolutionize the field by improving diagnostic accuracy, efficiency, and patient outcomes. AI algorithms can be trained to analyze radiographic images, detect abnormalities, and provide quantitative measurements with a high degree of accuracy. By leveraging AI technologies, radiologists can streamline their workflow, reduce interpretation errors, and enhance the quality of patient care. This research project will delve into the current state of AI applications in radiography, including the development of AI algorithms, their integration into existing radiology systems, and their impact on clinical practice. The project will also investigate the challenges and limitations associated with AI technology in radiography, such as data privacy concerns, algorithm bias, and the need for continuous validation and improvement. Furthermore, the research will explore the potential benefits of AI in improving diagnostic accuracy in radiography, including the early detection of diseases, personalized treatment planning, and enhanced communication between healthcare professionals. The project will also examine the ethical and legal implications of using AI in radiography, such as patient consent, data security, and regulatory compliance. Overall, this research aims to provide valuable insights into the use of artificial intelligence in radiography and its potential to transform the field of medical imaging. By investigating the opportunities and challenges associated with AI technology, this project seeks to contribute to the ongoing discussion on how to harness the power of AI to improve diagnostic accuracy and patient care in radiography.