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12:00 AM - TEDMED 2017
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Raleigh Health IT Summit
2017-10-19 - 2017-10-20    
All Day
About Health IT Summits Renowned leaders in U.S. and North American healthcare gather throughout the year to present important information and share insights at the Healthcare [...]
Connected Health Conference 2017
2017-10-25 - 2017-10-27    
All Day
The Connected Life Journey Shaping health and wellness for every generation. Top-rated content Valued perspectives from providers, payers, pharma and patients Unmatched networking with key [...]
TEDMED 2017
2017-11-01 - 2017-11-03    
All Day
A healthy society is everyone’s business. That’s why TEDMED speakers are thought leaders and accomplished individuals from every sector of society, both inside and outside [...]
AMIA 2017 Annual Symposium
2017-11-04 - 2017-11-08    
All Day
Call for Participation We invite you to contribute your best work for presentation at the AMIA Annual Symposium – the foremost symposium for the science [...]
Events on 2017-10-19
Raleigh Health IT Summit
19 Oct 17
Raleigh
Events on 2017-10-25
Events on 2017-11-01
TEDMED 2017
1 Nov 17
La Quinta
Events on 2017-11-04
AMIA 2017 Annual Symposium
4 Nov 17
WASHINGTON
Articles News

Hospitals are now aware of the development process for several health AI technologies. Will anything change as a result?

EMR Industry

A new federal regulation mandates that certain health AI makers reveal information about bias, testing, and other topics.

They know what the ones and zeroes buzzing away in the background are up to, don’t they? Clinicians click away at workstations in hospitals.

In actuality, physicians and health systems frequently lack critical knowledge about the algorithms they use for tasks like anticipating the start of serious illnesses. Federal regulators now mandate that electronic health record (EHR) businesses provide clients with a wide range of information regarding artificial intelligence tools in their software, which proponents say is a positive start.

Clinicians should have been able to see a model card, often known as a “nutrition label,” since the beginning of January. This label should include information on the variables that go into a prediction, whether a tool has been evaluated in the real world, how the tool’s authors addressed potential bias, cautions about improper use, and more.