Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Radiological imaging diagnosis plays important roles in clinical patient management. Deep
learning with convolutional neural networks (CNNs) is recently gaining wide attention for its …

As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data
processing techniques. One such technique, deep learning (DL), has become a remarkably …

Artificial intelligence (AI) is the most revolutionizing development in the health care industry
in the current decade, with diagnostic imaging having the greatest share in such …

Deep learning is the state-of-the-art machine learning approach. The success of deep
learning in many pattern recognition applications has brought excitement and high …

Purpose Despite tremendous gains from deep learning and the promise of artificial
intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large …

Background The performance of a deep learning (DL) algorithm should be validated in
actual clinical situations, before its clinical implementation. Purpose To evaluate the …

Objectives This study aims to contribute to an understanding of the explainability of
computer aided diagnosis studies in radiology that use end-to-end deep learning by …

Deep learning has been applied to clinical applications in not only radiology, but also all
other areas of medicine. This review provides a technical and clinical overview of deep …