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.
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.
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.
Last week the U.K. government announced plans to pour £250 million (around $301.5 million) into a fledgling AI lab run by the National Health Service (NHS). The work is to focus on advancing medical science in various arenas, including cancer care and dementia. This week the skeptics started weighing in.
Only 51% of consumers feel optimistic or safe when it comes to AI infiltrating the healthcare space in the form of helping providers in diagnostic decision making and care management, according to a recent survey from Blumberg Capital.
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.
If IBM’s Watson goes down as an early failure of AI in healthcare, the fumble may be recorded as an unforced error made by humans who were determined to position the company as the first serious player on the field.
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.