Implementation of Artificial Intelligence in Radiography: Enhancing Image Interpretation and Workflow Efficiency

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.