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Investigating the Use of Artificial Intelligence in Improving Diagnostic Accuracy in Radiography.

 

Table Of Contents


Chapter 1

: Introduction 1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objectives of Study
1.5 Limitations 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 Introduction to Literature Review
2.2 Importance of Artificial Intelligence in Radiography
2.3 Previous Studies on Diagnostic Accuracy in Radiography
2.4 Applications of AI in Radiography
2.5 Challenges and Limitations of AI in Radiography
2.6 Current Trends in Radiography Technology
2.7 Comparison of Traditional Methods and AI in Radiography
2.8 Ethical Considerations in Implementing AI in Radiography
2.9 Future Prospects of AI in Radiography
2.10 Summary of Literature Review

Chapter 3

: Research Methodology 3.1 Introduction to Research Methodology
3.2 Research Design
3.3 Data Collection Methods
3.4 Sampling Techniques
3.5 Data Analysis Procedures
3.6 Validation Methods
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter 4

: Discussion of Findings 4.1 Introduction to Discussion
4.2 Analysis of Diagnostic Accuracy Improvement with AI
4.3 Comparison of Results with Previous Studies
4.4 Interpretation of Data Findings
4.5 Implications of Findings on Radiography Practice
4.6 Discussion on Limitations Encountered
4.7 Recommendations for Future Research

Chapter 5

: Conclusion and Summary 5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to Radiography Field
5.4 Implications for Practice
5.5 Recommendations for Implementation
5.6 Areas for Future Research

Thesis Abstract

Abstract
This thesis investigates the utilization of artificial intelligence (AI) to enhance diagnostic accuracy in radiography. The integration of AI technologies in healthcare has garnered significant attention due to its potential to revolutionize traditional medical practices. In this study, the focus is on the specific application of AI in radiography, aiming to assess its impact on diagnostic accuracy and ultimately improve patient outcomes. The research methodology involves a comprehensive literature review to explore existing studies, methodologies, and technologies related to AI in radiography. Furthermore, empirical data is collected through interviews and surveys with radiologists and AI experts to gain insights into current practices and future potential. The findings reveal the benefits and challenges associated with implementing AI in radiography, highlighting its potential to augment diagnostic accuracy, reduce error rates, and enhance overall efficiency. The discussion delves into the implications of these findings, emphasizing the need for proper training, integration, and regulation to maximize the benefits of AI in radiography. This study contributes to the growing body of research on AI in healthcare and provides valuable insights for healthcare professionals, policymakers, and researchers seeking to leverage AI technologies for improving diagnostic accuracy in radiography.

Thesis Overview

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