Thesis Abstract

Abstract
The advancement of Artificial Intelligence (AI) technology has revolutionized various industries, including healthcare. This thesis explores the application of AI in radiography to enhance diagnostic accuracy. The primary aim of this study is to investigate how AI algorithms can be integrated into radiography processes to improve the interpretation of medical images and aid in accurate diagnosis. By leveraging AI capabilities, radiologists can benefit from enhanced efficiency, reduced error rates, and ultimately, better patient outcomes. The research begins with an introduction that provides background information on the use of AI in radiography and outlines the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review in Chapter two examines ten key studies related to AI applications in radiography, highlighting the current state of the art and identifying gaps in existing research. Chapter three presents the research methodology, detailing the approach, data collection methods, AI algorithms utilized, validation techniques, and ethical considerations. The methodology section also discusses the selection criteria for the study sample and the process of data analysis. In Chapter four, the findings of the study are comprehensively discussed, focusing on the impact of AI integration on diagnostic accuracy in radiography. The results highlight the effectiveness of AI algorithms in assisting radiologists with image interpretation, detecting abnormalities, and providing accurate diagnoses. Additionally, this chapter explores the challenges and opportunities associated with the implementation of AI in radiography. Finally, Chapter five offers a conclusion and summary of the thesis, emphasizing the key findings, implications, and recommendations for future research and clinical practice. The study concludes that the application of AI in radiography holds great promise for improving diagnostic accuracy and enhancing patient care. By harnessing the power of AI technologies, healthcare providers can achieve more precise and efficient radiological assessments, leading to better outcomes for patients. In conclusion, this thesis contributes to the growing body of knowledge on the integration of AI in radiography and its potential to transform the field of medical imaging. The findings underscore the importance of continued research and innovation in leveraging AI tools to enhance diagnostic accuracy and improve healthcare delivery.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" focuses on the integration of artificial intelligence (AI) technology into the field of radiography to enhance diagnostic accuracy. Radiography plays a crucial role in the detection and diagnosis of various medical conditions through the use of imaging techniques such as X-rays, CT scans, and MRIs. However, the interpretation of these images can be complex and subjective, leading to potential errors and variability in diagnoses. The introduction of AI in radiography offers promising solutions to address these challenges by leveraging machine learning algorithms to analyze medical images quickly and accurately. AI systems can assist radiologists in detecting abnormalities, identifying patterns, and providing quantitative analysis, ultimately leading to improved diagnostic accuracy and patient outcomes. The research will begin with a comprehensive literature review to explore existing studies and technologies related to AI in radiography. This review will cover topics such as the development of AI algorithms for image analysis, the integration of AI systems into radiology workflows, and the impact of AI on diagnostic accuracy and efficiency. The methodology section will outline the research design, data collection methods, and analysis techniques employed in the study. It will detail how AI algorithms will be trained and validated using a dataset of medical images to evaluate their performance in detecting and diagnosing various medical conditions. The discussion of findings will present the results of the study, including the performance metrics of the AI algorithms, comparisons with traditional diagnostic methods, and potential limitations or challenges encountered during the research process. The findings will be interpreted in the context of existing literature and implications for future research and clinical practice. In conclusion, the project aims to demonstrate the potential benefits of integrating AI technology into radiography for improved diagnostic accuracy. By harnessing the power of AI to assist radiologists in image analysis and interpretation, healthcare providers can enhance the quality and efficiency of patient care, leading to better outcomes and ultimately contributing to the advancement of medical imaging practices.