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AI-based Diagnostic X-Ray Quality Assurance
Author(s): Prosper Mbire (Midlands State University, Zimbabwe), Kennedy Chitiza (Midlands State University, Zimbabwe), Kudakwashe Peace Dzingirai (Midlands State University, Zimbabwe), Brian Sadock (Midlands State University, Zimbabwe)and Tafadzwanashe Lewis Dube (Midlands State University, Zimbabwe)
Copyright: 2025
Pages: 28
EISBN13: 9798337355566
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Abstract
The integration of artificial intelligence (AI) in diagnostic X-ray quality assurance (QA) represents a transformative advancement medical imaging. This paper explores the application of AI algorithms in enhancing the accuracy and efficiency of QA processes in X-ray diagnostics. We discuss various AI techniques, including machine learning and deep learning, that automate the assessment of image quality and detect anomalies, thereby reducing the reliance on manual evaluations. The implementation of AI-driven systems not only improves diagnostic accuracy but also streamlines workflows, minimizes radiation exposure, and supports radiologists in clinical decision-making. Challenges such as data quality, algorithm interpretability, and regulatory compliance are also addressed. Our findings suggest that AI-based QA systems can significantly enhance patient outcomes and operational efficiency in diagnostic radiology, paving the way for future innovations in medical imaging.
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