People with Type 1 diabetes may soon be able to count on an algorithm to keep their blood sugar levels within a healthy range, as research to tap the power of Big Data for automating personalized glucose monitoring and insulin delivery is underway. 

Unionized workers from one of the nation’s largest health systems have voted to strike against the healthcare organization in early October amid ongoing labor negotiations.

Drugmakers, wanting to develop new products informed by disease insights, are snapping up genetic profiles of hospital patients and spending hundreds of millions of dollars to get the data. But the practice is throwing into question who should control this “valuable genetic data."

When trained on routine health data and observation notes gathered by homecare aides, AI can be used to anticipate medical emergencies in the elderly one to two weeks ahead of an incident. The advance insights can both guide preventive care and save on unnecessary hospital and transportation costs.

GE Healthcare has joined an Australian AI-powered project aimed improving the diagnosis and monitoring of brain aneurysms.

One of the medical specialties highly hopeful in AI’s potential to guide care is neurosurgery. That’s because patients with traumatic brain injuries often present care teams and family members with an especially thorny decision. 

Machine learning can accurately predict which patients will not live beyond 30 days after discharge from the ER, giving these patients time to discuss end-of-life care with family members and hospice professionals.

Patients whose blood glucose levels spike during surgery are at heightened risk for poor overall outcomes. A new AI tool has proven effective at predicting, prior to surgery, which patients will have the problem while under the knife.