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.

Sifting the literature for real-world challenges thwarting adoption of clinical AI across medicine, a team of biomedical engineers and computer scientists has identified and fleshed out an exemplary use case.

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

Along with AI and machine learning, the list may include virtual and augmented reality, 3D printing, robotics and other technologies currently changing healthcare delivery.

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

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

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

The system hit 88% accuracy at optimizing stimulation settings, as confirmed by brain-response patterns on neuroimaging as well as visibly observable symptom improvement in patients with Parkinson’s disease.