Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) in radiography and conducts a comparative study to evaluate its impact on diagnostic accuracy and efficiency. The integration of AI technologies in radiography has gained significant attention in recent years due to its potential to enhance diagnostic processes and improve patient outcomes. This research project aims to investigate the effectiveness of AI algorithms in assisting radiographers in interpreting medical images and diagnosing various conditions. The study will begin by providing an overview of the background of AI in radiography, highlighting the advancements in technology and the potential benefits it offers to the field. The problem statement will address the current challenges faced in radiography, such as the increasing workload and the need for more accurate and timely diagnoses. The objectives of the study will outline the specific goals and research questions that will guide the investigation. The research methodology section will detail the approach taken to conduct the comparative study, including the selection of AI algorithms, data collection methods, and evaluation criteria. Various aspects of the methodology, such as study design, data analysis techniques, and ethical considerations, will be discussed to ensure the validity and reliability of the findings. The findings of the study will be presented and critically analyzed in the discussion section, focusing on the comparative evaluation of diagnostic accuracy and efficiency between traditional radiography practices and AI-assisted methods. The results will be interpreted in the context of existing literature and theoretical frameworks to provide a comprehensive understanding of the implications of AI in radiography. In conclusion, the study will summarize the key findings, implications, and limitations of the research. The significance of the study lies in its contribution to the growing body of knowledge on the integration of AI in radiography and its potential to revolutionize diagnostic practices in healthcare settings. This thesis aims to bridge the gap between traditional radiography methods and AI technologies, providing insights into the future of diagnostic imaging and patient care. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Efficiency, Comparative Study, Healthcare Technology, Medical Imaging.

Thesis Overview

Overview: The project "Implementation of Artificial Intelligence in Radiography: A Comparative Study of Diagnostic Accuracy and Efficiency" aims to investigate the integration of artificial intelligence (AI) technology in the field of radiography. Radiography plays a crucial role in medical diagnostics, providing detailed images of internal structures to aid in the detection and diagnosis of various health conditions. With the rapid advancements in AI technology, there is a growing interest in utilizing AI algorithms to assist radiographers in interpreting images more accurately and efficiently. This research project focuses on comparing the diagnostic accuracy and efficiency of traditional radiography methods with those enhanced by AI technology. By conducting a comparative study, the project aims to evaluate the performance of AI algorithms in analyzing radiographic images and detecting abnormalities when compared to the interpretations of human radiographers. The study will also assess the time taken for image analysis, the level of accuracy in diagnosis, and the overall efficiency of the radiography process when AI is integrated. Key Research Objectives: 1. To explore the current landscape of AI technology in radiography and its potential applications. 2. To investigate the impact of AI algorithms on the diagnostic accuracy of radiographic images. 3. To evaluate the efficiency of AI-assisted radiography in terms of time-saving and resource optimization. 4. To compare the performance of AI algorithms with human radiographers in detecting abnormalities in radiographic images. 5. To assess the challenges and limitations associated with the implementation of AI in radiography. Methodology: The research methodology involves a combination of quantitative and qualitative approaches to achieve the research objectives. Data collection will include the analysis of radiographic images using AI algorithms and the manual interpretation by experienced radiographers. Comparative analysis will be conducted to measure the accuracy and efficiency of both methods. Surveys and interviews with radiography professionals will also be conducted to gather insights on their experiences with AI technology in radiography. Expected Outcomes: The findings of this research project are expected to provide valuable insights into the effectiveness of AI technology in enhancing the diagnostic accuracy and efficiency of radiography. The results will contribute to the body of knowledge on the integration of AI in healthcare practices, particularly in the field of radiography. The project aims to highlight the potential benefits and challenges of implementing AI in radiography and provide recommendations for optimizing the use of AI technology to improve patient outcomes. Conclusion: In conclusion, the project "Implementation of Artificial Intelligence in Radiography: A Comparative Study of Diagnostic Accuracy and Efficiency" seeks to bridge the gap between traditional radiography practices and the advancements in AI technology. By conducting a comparative study, this research aims to explore the potential of AI algorithms in revolutionizing the field of radiography and enhancing the quality of patient care.