Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the integration of artificial intelligence (AI) emerging as a promising avenue for enhancing 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 study begins with an introduction to the background and context of AI in radiography, highlighting the current challenges in diagnostic accuracy and the need for innovative solutions. The problem statement underscores the limitations of traditional radiographic techniques and the growing demand for more accurate and timely diagnoses. The objectives of the study are to evaluate the effectiveness of AI in improving diagnostic accuracy, identify the limitations and challenges associated with its implementation, and assess the scope and significance of its impact on radiography practice. The research methodology employed encompasses a comprehensive literature review of existing studies on AI in radiography, followed by the development of a framework for evaluating AI systems in a clinical setting. The study also incorporates interviews and surveys with radiography professionals to gather insights on their experiences and perspectives regarding AI integration. The findings of the study reveal that AI technologies have the potential to significantly enhance diagnostic accuracy in radiography by assisting radiographers in interpreting imaging data and detecting abnormalities with greater precision. However, challenges such as data security, ethical considerations, and regulatory compliance pose barriers to the widespread adoption of AI in clinical practice. The discussion of findings delves into these challenges and proposes recommendations for overcoming them, emphasizing the importance of collaboration between healthcare providers, technology developers, and regulatory bodies. In conclusion, the study underscores the transformative potential of AI in radiography for improving diagnostic accuracy and patient outcomes. By leveraging AI technologies to augment radiographic interpretation, healthcare providers can deliver more accurate and timely diagnoses, leading to enhanced patient care and treatment outcomes. The thesis contributes to the growing body of research on AI in healthcare and provides valuable insights for radiography professionals, policymakers, and stakeholders seeking to leverage AI technologies for improved diagnostic accuracy in radiography practice.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to enhance diagnostic accuracy. Radiography plays a crucial role in medical imaging, providing valuable insights into various health conditions. However, the interpretation of radiographic images can be complex and subjective, leading to potential errors in diagnosis. By leveraging AI algorithms and machine learning techniques, this research seeks to develop a system that can assist radiographers in interpreting images more accurately and efficiently. The research will begin with a comprehensive review of existing literature on AI applications in radiography, highlighting the benefits and challenges associated with these technologies. This literature review will provide a foundation for understanding the current state of AI in radiography and identify gaps in the existing research that can be addressed through this study. The methodology chapter will outline the research design, data collection methods, and AI algorithms selected for the study. The research will involve collecting a diverse set of radiographic images and training the AI model to recognize patterns and abnormalities indicative of various medical conditions. The evaluation of the AI system will be conducted using test datasets and compared with traditional radiographic interpretations to assess its diagnostic accuracy and efficiency. The discussion of findings chapter will present the results of the study, focusing on the performance of the AI system in accurately diagnosing medical conditions from radiographic images. The findings will be analyzed in relation to the existing literature and implications for clinical practice will be discussed. The limitations of the study, such as dataset size and algorithm complexity, will also be addressed, along with recommendations for future research in this area. In conclusion, this research aims to demonstrate the potential of AI technologies in improving diagnostic accuracy in radiography. By developing an AI system that can assist radiographers in interpreting images more effectively, this study seeks to enhance the quality of patient care and contribute to advancements in medical imaging practices.