Medicine and deep learning-based artificial intelligence (AI) engineering represent two
distinct fields each with decades of published history. The current rapid convergence of …

The application of deep learning (DL) in medicine introduces transformative tools with the
potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring …

This is a consolidated look at the applications of various deep learning techniques in the
field of radiology. For the past few years, deep learning has pervaded every field and the …

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 …

In the present article, we provide an overview on the basics of deep learning in terms of
technical aspects and steps required to launch a deep learning research. Deep learning is a …

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

New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …

Medicine and artificial intelligence (AI) engineering represent two distinct fields each with
decades of published history. With such history comes a set of terminology that has a …

Since 2000, there have been more than 8000 publications on radiology artificial intelligence
(AI). AI breakthroughs allow complex tasks to be automated and even performed beyond …

Background The development of deep learning (DL) algorithms is a three-step process—
training, tuning, and testing. Studies are inconsistent in the use of the term “validation”, with …