Brainstorming nifty tech tools is only part of the battle to getting them used on the hospital floor. For that a broadly multidisciplinary team in Durham, N.C., has brought aboard some unlikely experts.

Three machine learning algorithms have identified patients likely to suffer extreme pain following surgery with about 80% accuracy each.

Nearly 90% of U.S. gastroenterologists are open to using AI for help performing high quality colonoscopies. And of these, 85% believe that computer-assisted polyp detection (CADe) stands to improve their endoscopic performance.

More than a week after protectively disconnecting all online systems in the U.S. following a Sept. 27 cyberattack, a multistate, two-nation health system is coming back online.

Along with the unprecedented challenges COVID-19 has brought to U.S. hospitals in 2020 are some older sore spots that the pandemic has only inflamed.

Middle-aged individuals of modest means widely availed themselves of formal long-term care after Obamacare funded the expansion of Medicaid in participating states. 

Demand is sure to outstrip supply when a safe and effective COVID vaccine initially enters mass production. First in line to receive the inoculation should be the 5% slice of the population doing essential work to help fight the pandemic.

AI is a key part of the plan as two groups with little in common come together over health disparities that have only been worsened by the COVID-19 pandemic.

The COVID-19 pandemic is a bad dream in the truest sense of the word—and AI helps to prove it, a team of researchers assert in a study published online Oct. 1 in Frontiers in Psychology.

Researchers at Columbia University have developed a machine learning algorithm that identifies and predicts gender-based differences in adverse reactions to drugs.