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.

Explainable AI is almost as sharp as human experts when the cause is simple and straightforward, as with ingestion of a single common cleaning product.

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

Google Health is launching a free app that uses AI trained on more than 16,000 clinical dermatology cases and can identify hundreds of skin conditions, including cancers, with accuracy comparable to that of board-certified dermatologists in the U.S.

Upon examining a skin lesion they suspected of being malignant, few dermatologists—only 8%—would hold back from performing a biopsy if an AI tool disagreed, classifying it as benign.

AI company Exscientia has received a vote of confidence approaching $1.2 billion in value from Bristol-Myers Squibb.

A novel AI-based model for clinical decision support has bested established machine-learning models at predicting how patients with type-2 diabetes mellitus will respond to various categories of therapeutic drugs.

In a tryout trial, the technique quickly and accurately predicted shock as well as the need for early massive transfusion and major surgery.

Bioengineers have developed a brain-computer interface that replicates the sense of touch, allowing a robotic arm and hand to not only receive command signals from the brain but also send back signals of stimulation.