Since news first broke of the coronavirus outbreak, China’s government has received a massive number of donations from the public and various healthcare organizations—but the distribution of those donations has been disorganized.

According to a new AI simulation, the Wuhan coronavirus could kill 52.9 million people within 45 days—and infect 2.5 billion overall.

Radiology researchers are turning to deep learning (DL) technology to make NLP even more effective—and it’s a growing trend that shows no signs of slowing down.  

Generative adversarial networks (GANs), a fairly new breakthrough in AI, are capable of creating fake images that look incredibly real.

Blockchain technology can play a key role in the management of medical records, according to a new commentary published in npj Digital Medicine.

Machine learning (ML) can provide significant value in the field of palliative care. However, researchers still have a lot of unexplored ground to cover before the technology reaches its full potential.

Chun Yuan, PhD, has received a two-year, $200,000 grant from the American Heart Association’s Institute for Precision Cardiovascular Medicine for his work on using AI to detect blocked arteries and cardiovascular risk.

Researchers have developed a multitask deep learning model that can effectively assess signs of hip osteoarthritis in x-rays, sharing their findings in Radiology.

Researchers have developed a smart patch for insulin delivery, publishing their findings in Nature Biomedical Engineering.

The rise of AI in healthcare—especially radiology—has launched countless conversations about ethics, bias and the difference between “right” and “wrong.”