A team-based protocol for treating cardiogenic shock helped one center boost its 30-day survival rates for those patients by nearly 20 percentage points over a two-year period, researchers reported in the Journal of the American College of Cardiology.

AI is poised to make a sizable impact on healthcare this year, but we’ll likely see the greatest dent in the areas of medical imaging, diagnostics and suicide risk prediction, according to data presented at the Partners HealthCare World Medical Innovation Forum in Boston.

A rare and difficult-to-diagnose genetic condition that raises LDL (bad) cholesterol to dangerous levels is now vulnerable to an AI tool.

As incidents producing mass casualties can occur anywhere at any time, leadership at trauma centers of all levels do well to prepare. A good place to start is learning from the experiences of urban hospitals dealing with shooting victims every day or close to it.

Researchers have identified an approach for more accurately diagnosing schizophrenia using AI, bringing some objectivity to the field of neurodevelopmental disorder diagnosis, according to a study published in the May edition of Artificial Intelligence in Medicine.

A deep learning model trained on more than 1.5 million electrocardiograms and developed by a team at the Mayo Clinic improved detection rates for hyperkalemia in patients with chronic kidney disease (CKD), according to a study published April 3 in JAMA Cardiology.

Mental health researchers at Harvard and the University of Pennsylvania have developed a novel machine learning technique for predicting how bipolar patients will respond to two drugs commonly prescribed to treat the disorder, according to a study running in Bipolar Disorders.

The CDC has confirmed nearly 600 cases of a deadly multi-drug-resistant fungus in the U.S. as of February 28, with most cases concentrated in New Jersey, New York and Illinois.