Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) in radiography to enhance image interpretation and workflow efficiency. The integration of AI technologies in radiography has the potential to revolutionize the field by improving diagnostic accuracy, reducing interpretation time, and streamlining workflow processes. This research aims to investigate the impact of AI in radiography and evaluate its effectiveness in enhancing image interpretation and workflow efficiency. The study begins with a comprehensive review of existing literature on AI applications in radiography, highlighting the benefits and challenges associated with its implementation. Through a systematic analysis of ten key studies, various AI algorithms, such as deep learning and machine learning, are examined for their potential in improving image interpretation accuracy and workflow efficiency in radiography. The research methodology section outlines the approach taken to assess the impact of AI in radiography. The study utilizes a mixed-methods approach, combining quantitative analysis of radiographic images with qualitative feedback from radiographers and clinicians. Data collection methods include image analysis software, surveys, and interviews to gather insights on the effectiveness of AI tools in clinical practice. Findings from the study reveal that the implementation of AI technologies in radiography has led to a significant improvement in image interpretation accuracy and workflow efficiency. AI algorithms demonstrated high sensitivity and specificity in detecting abnormalities in radiographic images, leading to more precise diagnoses and treatment planning. Moreover, the integration of AI tools streamlined workflow processes, reducing interpretation time and enhancing overall productivity in radiology departments. The discussion section delves into the implications of the study findings, emphasizing the potential benefits of AI in radiography for both patients and healthcare providers. The ethical considerations and challenges associated with AI integration in radiography are also addressed, highlighting the importance of maintaining patient privacy and data security in AI-driven healthcare environments. In conclusion, this thesis underscores the transformative potential of AI in radiography for enhancing image interpretation and workflow efficiency. By leveraging AI technologies, radiographers and clinicians can improve diagnostic accuracy, optimize workflow processes, and ultimately enhance patient care outcomes in radiology practice. The study contributes to the growing body of research on AI applications in healthcare and provides valuable insights for future advancements in radiography practice.

Thesis Overview

The research project titled "Implementation of Artificial Intelligence in Radiography: Enhancing Image Interpretation and Workflow Efficiency" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to improve the efficiency and accuracy of image interpretation and workflow processes. Radiography plays a crucial role in diagnosing various medical conditions by capturing and interpreting images of the internal structures of the human body. However, the interpretation of radiographic images can be time-consuming and prone to human errors, which may impact patient care and diagnosis accuracy. By leveraging AI algorithms and machine learning techniques, this research seeks to enhance the capabilities of radiographers in interpreting complex images more effectively and efficiently. The integration of AI in radiography has the potential to automate image analysis, assist in detecting abnormalities, and provide decision support to healthcare professionals. Moreover, AI can help streamline workflow processes, reduce interpretation time, and improve overall diagnostic accuracy. The research overview will delve into the current landscape of radiography and the challenges faced in image interpretation and workflow management. It will explore the advancements in AI technologies, such as deep learning and image recognition, and their applications in radiography. Furthermore, the research will investigate the potential benefits and limitations of implementing AI in radiography, considering factors such as data privacy, ethical considerations, and regulatory compliance. Through a comprehensive literature review and empirical research methodology, this study aims to evaluate the impact of AI implementation on radiography practices, including its effectiveness in enhancing image interpretation, workflow efficiency, and overall patient care outcomes. The findings of this research are expected to contribute valuable insights to the field of radiography and inform healthcare institutions about the opportunities and challenges associated with adopting AI technologies in diagnostic imaging. Overall, the project "Implementation of Artificial Intelligence in Radiography: Enhancing Image Interpretation and Workflow Efficiency" seeks to bridge the gap between traditional radiography practices and cutting-edge AI technologies to revolutionize the field of medical imaging and improve healthcare delivery for patients.