It’s a contradiction that’s long slowed the forward march of artificial intelligence in medicine: Machine learning models need to be trained on lots of diverse data from hospitals around the world — but those hospitals are often reluctant to ship out their data due to privacy concerns, legal issues, and a cautious culture.

One promising way to get around that problem is a technique known as federated learning, which allows models to be trained without having to share data to a central server or in the cloud. Now, the approach is being put to the test in an ambitious project to build an AI system from thousands of brain tumor scans from several dozen hospitals and research institutions around the globe.

Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free!

GET STARTED

What is it?

STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.

What's included?

  • Daily reporting and analysis
  • The most comprehensive industry coverage from a powerhouse team of reporters
  • Subscriber-only newsletters
  • Daily newsletters to brief you on the most important industry news of the day
  • STAT+ Conversations
  • Weekly opportunities to engage with our reporters and leading industry experts in live video conversations
  • Exclusive industry events
  • Premium access to subscriber-only networking events around the country
  • The best reporters in the industry
  • The most trusted and well-connected newsroom in the health care industry
  • And much more
  • Exclusive interviews with industry leaders, profiles, and premium tools, like our CRISPR Trackr.

A roundup of STAT’s top stories of the day in science and medicine

Privacy Policy