Thesis Abstract

Abstract
The integration of artificial intelligence (AI) in radiography has revolutionized the field, offering new opportunities for enhancing diagnostic accuracy and efficiency. This thesis investigates the impact of AI on radiography through a comparative study, analyzing the benefits, challenges, and implications of AI technology in radiological imaging. The research explores the current landscape of AI applications in radiography, comparing traditional radiographic techniques with AI-enhanced approaches. By examining the advantages and limitations of AI in radiography, this study aims to provide insights into how AI can improve diagnostic accuracy, reduce interpretation errors, and enhance patient care outcomes. The methodology involves a comprehensive literature review, data collection from radiography professionals, and analysis of case studies showcasing AI integration in radiological practices. The findings reveal the potential of AI to revolutionize radiography by providing faster, more accurate diagnoses, optimizing workflow efficiency, and enabling personalized treatment plans. However, challenges such as data privacy, regulatory compliance, and ethical considerations must be addressed to ensure the responsible and effective implementation of AI in radiography. The comparative analysis highlights the differences between AI-assisted radiography and conventional practices, emphasizing the need for continuous education and training to maximize the benefits of AI technology. The implications of this study extend to radiography professionals, healthcare institutions, regulatory bodies, and policymakers, providing valuable insights into the transformative role of AI in radiological imaging. Overall, this research contributes to the ongoing discourse on the integration of AI in radiography, offering a roadmap for harnessing AI technology to improve the quality and efficiency of radiological services. Word Count 200

Thesis Overview

The project titled "Investigating the Impact of Artificial Intelligence on Radiography: A Comparative Study" aims to explore the influence of artificial intelligence (AI) on the field of radiography. As technology continues to advance rapidly, AI has emerged as a powerful tool with the potential to revolutionize various industries, including healthcare. In the context of radiography, AI holds the promise of improving diagnostic accuracy, efficiency, and patient outcomes. This comparative study will delve into the current applications of AI in radiography and compare them with traditional methods to assess the benefits and challenges associated with AI integration. By conducting a thorough analysis, the research aims to provide valuable insights into the extent to which AI can enhance the practice of radiography and contribute to better healthcare delivery. The study will involve a comprehensive literature review to examine existing research and developments in AI technology within the field of radiography. Various AI algorithms and tools utilized in radiological imaging interpretation, diagnosis, and treatment planning will be explored to understand their capabilities and limitations. Additionally, the study will investigate the impact of AI on radiographer workflow, patient care, and overall healthcare system efficiency. Furthermore, the research will involve a comparative analysis of AI-assisted radiography practices in different healthcare settings to identify best practices and potential areas for improvement. By comparing the performance of AI systems with that of human radiographers, the study aims to evaluate the accuracy, speed, and reliability of AI-generated results in radiological imaging interpretation. Through this comparative study, the project seeks to address key research questions such as the effectiveness of AI in detecting abnormalities, the level of trust and acceptance among radiographers, and the implications of AI integration on professional roles and responsibilities within the field of radiography. The findings of this research are expected to provide valuable insights for healthcare professionals, policymakers, and stakeholders seeking to leverage AI technology to enhance radiography practices and improve patient care outcomes.