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

Objective To systematically examine the design, reporting standards, risk of bias, and claims
of studies comparing the performance of diagnostic deep learning algorithms for medical …

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

Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …

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

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 …

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 …

Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

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 …