AI FOR AUTOMATED DIAGNOSIS FROM RADIOLOGY DATA

Abstract

Automated diagnosis from radiology data has become increasingly important in modern healthcare, offering the potential to enhance diagnostic accuracy, reduce interpretation time, and support clinical decision-making. Radiology modalities such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) generate vast amounts of data that can be challenging for human interpretation alone. Artificial intelligence (AI) and deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as powerful tools for automated analysis, enabling detection, classification, and localization of various pathologies. This paper reviews current AI methodologies for automated diagnosis in radiology, discusses challenges including data variability, limited annotated datasets, and model interpretability, and explores the potential of AI-assisted systems to improve patient outcomes and optimize clinical workflows.