Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) in radiography to enhance the accuracy of diagnosis and improve patient care in medical imaging. The integration of AI technology in radiography has the potential to revolutionize the field by providing automated tools for image analysis, interpretation, and decision-making. This study investigates the current state of AI in radiography, its benefits, challenges, and future prospects. Chapter One 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 Two Literature Review 2.1 Overview of Artificial Intelligence in Radiography 2.2 Evolution of AI in Medical Imaging 2.3 AI Applications in Radiography 2.4 Benefits and Challenges of AI in Radiography 2.5 Current Trends and Future Prospects 2.6 AI Algorithms and Models in Radiography 2.7 Integration of AI with Radiology Workflow 2.8 AI-Assisted Diagnosis in Radiography 2.9 Ethical and Legal Implications of AI in Radiography 2.10 Comparative Analysis of AI Tools in Radiography Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection Methods 3.3 Data Analysis Techniques 3.4 AI Tools and Technologies Utilized 3.5 Sampling Methods 3.6 Study Population 3.7 Validation and Testing Procedures 3.8 Ethical Considerations Chapter Four Discussion of Findings 4.1 AI-Enhanced Image Analysis 4.2 AI-Assisted Diagnosis Accuracy 4.3 Impact on Radiology Workflow 4.4 Patient Outcomes and Care 4.5 Radiographer Training and Adoption 4.6 Cost-Effectiveness and Efficiency 4.7 User Acceptance and Satisfaction 4.8 Integration Challenges and Solutions Chapter Five Conclusion and Summary This thesis provides a comprehensive examination of the application of AI in radiography for improved diagnosis and patient care. The findings suggest that AI technologies hold great promise in enhancing the accuracy of radiographic interpretations, improving workflow efficiency, and ultimately benefiting patient outcomes. However, successful implementation requires addressing challenges such as data privacy, regulatory compliance, and staff training. The study concludes with recommendations for future research and practical implications for the integration of AI in radiography practice.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnosis and Patient Care" focuses on the integration of artificial intelligence (AI) technology in the field of radiography to enhance diagnostic accuracy and patient care outcomes. This research aims to explore how AI can be effectively utilized in radiography practices to improve the efficiency, speed, and accuracy of image interpretation, leading to better patient outcomes and reduced healthcare costs. Radiography plays a critical role in diagnosing various medical conditions by producing images of the internal structures of the body using various imaging modalities such as X-rays, CT scans, and MRIs. However, the process of interpreting these images can be time-consuming and prone to human error, leading to delays in diagnosis and potentially incorrect treatment decisions. By incorporating AI algorithms and machine learning techniques into radiography workflows, healthcare professionals can benefit from advanced image analysis tools that can assist in detecting abnormalities, identifying patterns, and providing quantitative data to support clinical decision-making. AI can also help in automating repetitive tasks, such as image segmentation and feature extraction, allowing radiologists to focus more on complex cases and patient care. Furthermore, the research will investigate the potential challenges and limitations associated with implementing AI in radiography, such as data privacy concerns, regulatory compliance, and the need for continuous training and validation of AI models. Strategies for overcoming these barriers will be explored to ensure the successful integration of AI technology into routine clinical practice. Overall, this research seeks to highlight the significant impact of AI on radiography services and its potential to revolutionize the way medical imaging is conducted, leading to more accurate diagnoses, personalized treatment plans, and ultimately, improved patient care outcomes.