Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
This thesis explores the Application of Artificial Intelligence (AI) in Radiography for Improved Diagnostic Accuracy. The integration of AI in radiography has revolutionized medical imaging practices by enhancing the accuracy and efficiency of diagnostics. The primary objective of this study is to investigate the impact of AI technologies on radiography and how they can be utilized to improve diagnostic outcomes. The research methodology involves a comprehensive literature review to analyze existing studies on AI applications in radiography. Various AI techniques such as machine learning, deep learning, and computer-aided diagnosis are examined to understand their roles in enhancing diagnostic accuracy. The study also includes a quantitative analysis of data collected from radiography departments implementing AI technologies. The findings reveal that AI algorithms can significantly improve the accuracy of radiographic interpretations by assisting radiologists in detecting abnormalities and making precise diagnoses. The implementation of AI tools in radiography has shown promising results in reducing interpretation errors, enhancing workflow efficiency, and ultimately improving patient outcomes. The discussion of findings highlights the potential benefits and challenges associated with the adoption of AI in radiography. Ethical considerations, data privacy concerns, and the need for continuous training of healthcare professionals are crucial factors to address when integrating AI technologies into radiographic practices. In conclusion, the Application of Artificial Intelligence in Radiography offers immense potential for enhancing diagnostic accuracy and optimizing patient care. The study emphasizes the importance of leveraging AI technologies to augment the capabilities of radiologists, ultimately leading to improved healthcare outcomes. Recommendations for future research include further exploration of AI applications in specialized imaging modalities and the development of standardized protocols for AI integration in radiography. Keywords Artificial Intelligence, Radiography, Diagnostic Accuracy, Machine Learning, Deep Learning, Computer-Aided Diagnosis.