Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) technologies in radiography has brought about significant advancements in the field of diagnostic imaging, aiming to enhance the accuracy and efficiency of medical diagnoses. This thesis explores the implementation of AI in radiography to improve diagnostic accuracy through the analysis of various imaging modalities and the application of AI algorithms. The research delves into the background of AI in healthcare, particularly in radiography, highlighting the potential benefits and challenges associated with this technology. The study identifies the current problems in traditional radiography practices, emphasizing the limitations and discrepancies that can arise in manual interpretation of medical images. By setting clear objectives, the research aims to investigate how AI can be effectively utilized to address these challenges and enhance diagnostic precision in radiography. The scope of the study encompasses the application of AI in different imaging techniques, such as X-rays, CT scans, and MRI, to analyze and interpret medical images with improved accuracy and consistency. Through an extensive literature review, the thesis examines existing studies and developments in AI applications for radiography, presenting a comprehensive analysis of the current state-of-the-art technologies and methodologies. The research methodology section outlines the approach taken to evaluate the performance of AI algorithms in radiographic image analysis, including data collection, processing, and validation procedures. Various metrics and evaluation criteria are utilized to assess the efficacy and reliability of AI systems in enhancing diagnostic accuracy in radiography. The findings of the study reveal the effectiveness of AI in improving diagnostic accuracy in radiography, showcasing the potential of AI algorithms to expedite the identification of abnormalities and assist radiologists in making accurate diagnoses. The discussion of findings section critically analyzes the results obtained from experimental evaluations, highlighting the strengths and limitations of AI technologies in radiographic image analysis. Insights are provided on the practical implications of integrating AI tools in clinical settings, emphasizing the importance of collaboration between AI systems and healthcare professionals to optimize diagnostic outcomes. In conclusion, this thesis underscores the significance of implementing AI in radiography for achieving improved diagnostic accuracy and enhancing patient care outcomes. The summary encapsulates the key findings and contributions of the research, emphasizing the transformative potential of AI technologies in revolutionizing radiographic practices. The study concludes with recommendations for future research directions and practical implications for integrating AI into routine clinical workflows, paving the way for a more efficient and accurate diagnostic process in radiography.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technology in the field of radiography to enhance diagnostic accuracy. Radiography plays a crucial role in medical imaging for diagnosing various conditions, and the utilization of AI has the potential to revolutionize this process by providing more accurate and efficient results. The research will delve into the background of the study, highlighting the current practices in radiography and the growing importance of AI in healthcare. It will address the problem statement, emphasizing the existing challenges in traditional diagnostic methods and the need for improved accuracy and efficiency. The objectives of the study will be outlined to guide the research towards specific goals, such as evaluating the effectiveness of AI algorithms in radiography and assessing their impact on diagnostic outcomes. The study will also acknowledge the limitations and scope of the research, clarifying the boundaries within which the investigation will be conducted. The significance of the study will be emphasized, showcasing the potential benefits of implementing AI in radiography, such as faster diagnosis, reduced errors, and improved patient outcomes. The structure of the thesis will be outlined to provide a roadmap for the research process, detailing the chapters and subtopics that will be covered. Through an extensive literature review, the research will explore existing studies and technologies related to AI in radiography. This review will encompass various aspects, including AI algorithms, machine learning techniques, image analysis methods, and their applications in medical imaging. By synthesizing this information, the study aims to build a comprehensive understanding of the current state-of-the-art in AI-driven radiography and identify gaps for further exploration. The research methodology section will detail the approach and techniques employed to investigate the research questions and achieve the study objectives. This will include data collection methods, experimental design, AI model development, validation processes, and performance evaluation criteria. By adopting a systematic and rigorous methodology, the study aims to ensure the reliability and validity of the research findings. In the discussion of findings chapter, the research will present and analyze the results obtained from implementing AI in radiography. This analysis will cover aspects such as diagnostic accuracy, efficiency improvements, comparison with traditional methods, challenges encountered, and potential areas for optimization. By critically evaluating the findings, the study aims to draw meaningful conclusions and insights that contribute to the advancement of AI in radiography. In the conclusion and summary chapter, the research will summarize the key findings, implications, and contributions of the study. It will also highlight the practical implications of implementing AI in radiography, addressing the implications for healthcare professionals, patients, and the broader healthcare system. The conclusion will also identify future research directions and recommendations for further enhancing the integration of AI technology in radiography practice. Overall, the project on the "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" seeks to advance the field of radiography by harnessing the power of AI to enhance diagnostic capabilities, improve patient care, and drive innovation in medical imaging.