Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements with the integration of artificial intelligence (AI) technologies, offering promising opportunities to enhance diagnostic accuracy and efficiency. This thesis explores the implementation of AI in radiography to improve diagnostic accuracy, focusing on its potential impact on healthcare outcomes and patient care. The research delves into the background of AI in radiography, highlighting the evolution of technology and its implications for the field. The study addresses the growing importance of accurate and timely diagnosis in healthcare settings, underscoring the need for innovative solutions to streamline radiographic processes. Through a comprehensive literature review, this thesis examines current practices and technologies in radiography, identifying gaps and challenges that AI can address. The review encompasses ten key areas, including AI applications in medical imaging, machine learning algorithms, image interpretation, and diagnostic decision support systems. By analyzing existing literature, the research provides a foundation for understanding the potential benefits and limitations of AI integration in radiography. The methodology chapter outlines the research approach and design, detailing the data collection methods, sample selection criteria, and analytical techniques employed. The study utilizes a mixed-methods approach, combining quantitative analysis of diagnostic accuracy metrics with qualitative assessment of user perceptions and experiences. By engaging radiography professionals and AI experts in the research process, the study aims to capture diverse perspectives on the implementation of AI in clinical practice. The discussion of findings chapter presents a detailed analysis of the research results, highlighting the impact of AI integration on diagnostic accuracy and clinical workflow. The findings reveal that AI technologies have the potential to enhance radiographic interpretation, reduce diagnostic errors, and improve overall patient outcomes. Moreover, the study identifies key factors influencing the successful implementation of AI in radiography, including data quality, algorithm performance, and user acceptance. In conclusion, this thesis summarizes the key findings and implications of implementing AI in radiography for improved diagnostic accuracy. The research underscores the transformative potential of AI technologies in healthcare settings, emphasizing the importance of collaboration between radiography professionals and AI developers. By leveraging the capabilities of AI for image analysis and decision support, radiographers can enhance their diagnostic capabilities and provide more accurate and timely diagnoses to patients. This thesis contributes to the growing body of knowledge on AI applications in radiography and offers insights for future research and practice in the field.

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 into radiography practice to enhance diagnostic accuracy and efficiency. This research overview provides a detailed explanation of the project, highlighting the significance, objectives, methodology, and expected outcomes. **Significance of the Study:** Radiography plays a crucial role in medical diagnosis and treatment planning by producing images of internal structures for clinical assessment. However, human error and variability in interpretation can impact the accuracy of diagnoses. By incorporating AI algorithms into radiography practice, healthcare professionals can benefit from advanced image analysis tools that improve diagnostic accuracy, reduce interpretation time, and enhance patient outcomes. **Objectives of the Study:** The primary objective of this research is to investigate the implementation of AI technology in radiography to improve diagnostic accuracy. Specific objectives include: 1. Assessing the current challenges and limitations in radiography practice. 2. Exploring the capabilities of AI algorithms in image analysis and interpretation. 3. Developing a framework for integrating AI technology into radiography workflow. 4. Evaluating the impact of AI implementation on diagnostic accuracy and efficiency. 5. Identifying opportunities for further research and advancement in AI-enabled radiography. **Methodology:** The research will adopt a mixed-methods approach, combining qualitative and quantitative data collection techniques. The study will involve literature review, case studies, interviews with radiography professionals, and implementation of AI algorithms in radiography settings. Data analysis will focus on comparing diagnostic outcomes before and after AI implementation, assessing the usability and acceptance of AI technology among healthcare professionals, and identifying factors influencing the success of AI integration in radiography practice. **Expected Outcomes:** It is anticipated that the implementation of AI technology in radiography will lead to improved diagnostic accuracy through automated image analysis, pattern recognition, and decision support systems. The research findings will provide insights into the benefits, challenges, and implications of AI-enabled radiography, contributing to the advancement of healthcare technology and clinical practice. The outcomes of this study will inform future research initiatives, policy development, and professional training programs in the field of radiography and medical imaging. In conclusion, the project "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to leverage AI technology to enhance the quality and efficiency of radiography services, ultimately benefiting patients, healthcare providers, and the healthcare system as a whole.