Industry researchers challenged a machine learning model to predict failure to heal at four, eight and 12 weeks after initial treatment.

Researchers have used unsupervised machine learning to predict disease-causing properties in more than 36 million genetic variants across more than 3,200 disease-related genes.

The model’s developers suggest algorithmic prognoses could be aggregated to guide overdose prevention at the local, county and regional levels.

New research shows horizontal gene transfer is predictable in bacteria by machine learning, a development that could lead to better weapons in the war against E. coli and other bacterial assailants that collaborate to conquer pharmacologic first responders.  

Could AI help produce a unifying concept of human disease—one that might help prevent, mitigate or cure everything from birth defects and rare cancers to immune disorders and neurological defects?

Famed billionaire Mark Cuban is entering the world of pharmacy benefit management. 

The AI development team was guided by a sports-medicine specialist dubbed “the go-to orthopedic surgeon for many of the greatest athletes on the planet.”

More than one-quarter of the U.S. adult population has Gastroesophageal Reflux Disease, or GERD, and the condition saddles as many as 20% of its sufferers with Barrett’s esophagus. The latter is a serious risk factor for esophageal cancer.