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

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

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

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.

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

Natural language processing of social media posts is useful for identifying depression, anxiety and suicidal thinking, but models trained on population data cannot discern long-term patterns in any one person’s state of mind.

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?