There’s no shortage of resources for healthcare workers who wish they knew AI well enough to talk shop with the technology pros who develop the models. The problem is weeding through the offerings to get to what will really work for you. 

Discussions of AI governance may cause many an eye to glass over, but the discipline is as crucial to the ascent of AI in healthcare as big training datasets drawn from diverse patient populations.

For whatever reason, the grownups seem to get all the attention when talk turns to AI in healthcare. All the kiddos get to do is look on and listen in. Now comes a worthy little effort to balance the seesaw.

It’s going to take a multinational effort for the global AI community—such as it is—to avoid the emergence of a “fragmented AI landscape.” 

Cardiovascular devices are more likely to be in a Class I recall than any other device type. The FDA's approval process appears to be at least partially responsible, though the agency is working to make some serious changes. We spoke to a researcher who has been tracking these data for years to learn more. 

The U.S. Food and Drug Administration has its hands full making sure medical AI products are safe, efficacious and trustworthy before they hit the market. The rise of ever-more-innovative iterations of the technology—not least generative AI—is only adding to the burden. 

Among AI’s most watchful stakeholders are healthcare organizations in need of AI talent and AI talent in need of work in healthcare. Both groups need to keep up with the technology in its present as well as future iterations.

Some 60% of IT professionals and 51% of operational leaders admit they’re reluctant to involve clinicians in software decisions.