Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.

Empathetic, affable, visually unthreatening and coolly competent in several healthcare tasks, a newly trained nurse named Grace has made a head-turning debut.

The data will draw on everything from census findings to driving habits gathered from vehicle sensors to—arguably most consequentially—medical records.

Black-box AI should be barred from reading medical images in clinical settings because machine learning, like human thinking, tends to take diagnostic shortcuts. 

Healthcare AI has potential not only for neutralizing its inherent algorithmic bias but also for personalizing its outputs to help humans address health inequities.

Researchers have developed a nanopore that uses AI to detect COVID-19 and other viruses in easily obtained saliva specimens.

Researchers have used explainable AI to uncover factors that influence inpatients’ opinions when they’re completing satisfaction surveys following hospitalization.

Many parents would let their children be read to by robots as long as the device didn’t project a little too much lifelikeness.

When the network interpreted complete videos from 42 consecutive patients, it boosted detection rates by as much as 50% over physician-alone reads.

AI is poised to help settle an argument that’s been roiling academic psychiatry for more than a century: Are bipolar disorder and schizophrenia two distinct diagnoses—or points along a single continuum?