Imperfect algorithms. Resistant clinicians. Wary patients. Divisive disparities. The plot ingredients of a flashy techno-thriller coming to a cineplex near you? No—just a few of the many worries that provider organizations take on when they move to adopt AI at scale. 

It’s more critical for U.S. healthcare to get medical AI right than to get it adopted far, wide and ASAP.

Scientists are people too. As such, when engaged in research projects using AI, they must resist the very human impulse to over-delegate tasks to algorithms.

Years will pass before the global economy’s healthcare sector sufficiently leverages AI to build major financial muscle off of it. And even then, industry players are likely to see gains well ahead of hospitals and health systems. 

A new scientific statement from the American Heart Association explores the many ways AI and machine learning are being used to improve care for heart patients.

As healthcare AI opens new avenues to improve care quality without unduly increasing operational costs, the technology also expands potential exposure to civil and criminal liabilities. And that’s not only for providers but also payers and suppliers.

The American Medical Association lays out eight in-demand AI use cases for which the organization says it has heard physicians “express particular enthusiasm.”

When asked to depict people by various descriptors, the image generator delivered lots of results that were unintentionally hilarious for their wild inaccuracy.