Machine learning algorithms can comb population-level patient databases to find individuals who might benefit by treatment for depression.

Machine learning is no better than physicians at predicting acute kidney injury (AKI) in the ICU, where it’s a sign of poor outcomes ahead as soon as it appears. However, the AI approach can help mitigate physicians’ tendency to overestimate risks and overtreat low-risk patients.

MyHealthTeams, a San Francisco-based creator of social networks for patients with chronic health conditions, has raised a fresh $9.44 million to expand its existing online spaces and launch new ones.

As speculation continues to swirl around AI’s forthcoming transformation of healthcare, fueling boom times in AI research, a review of the literature has turned up scant evidence the technology is benefiting patients at the consumer level.

VR is quickly becoming a new alternative to more traditional methods of pain management, NPR reported, with patients escaping their chronic pain by strapping on a VR headset and becoming immersed in a different reality.

Machine-learning analyses of satellite images can help identify communities needing healthcare services in some of the most remote parts of the planet, according to a study published Aug. 14 in the Journal of the American Medical Informatics Association.

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.