It’s been years since AI proponents started promising big returns on healthcare providers’ investments in the technology. The results have yet to catch up with the pitches. What’s the holdup? 

The pace of AI adoption is quickening in the sector, which includes biopharmaceuticals, digital health, clinical diagnostics and medical devices.

If you work with healthcare AI, you owe it to patients, families and communities to keep certain principles top of mind in everything you do. 

What makes an organization an AI leader? Demonstrating success in one of two pursuits. 

As debate simmers over how best to regulate AI, experts continue to offer guidance on where to start, how to proceed and what to emphasize. A new resource models its recommendations on what its authors call the “SETO Loop.”

With generative AI coming into its own, AI regulators must avoid relying too much on principles of risk management—and not enough on those of uncertainty management.

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.