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.

Because they learn as they go, machine learning models for drug discovery have to be continuously re-trained for changing conditions in drug production processes.

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

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

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

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

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.

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.