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Electronic Medical Records Boot Camp
2025-06-30 - 2025-07-01    
10:30 am - 5:30 pm
The Electronic Medical Records Boot Camp is a two-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of electronic health [...]
AI in Healthcare Forum
2025-07-10 - 2025-07-11    
10:00 am - 5:00 pm
Jeff Thomas, Senior Vice President and Chief Technology Officer, shares how the migration not only saved the organization millions of dollars but also led to [...]
28th World Congress on  Nursing, Pharmacology and Healthcare
2025-07-21 - 2025-07-22    
10:00 am - 5:00 pm
To Collaborate Scientific Professionals around the World Conference Date:  July 21-22, 2025
5th World Congress on  Cardiovascular Medicine Pharmacology
2025-07-24 - 2025-07-25    
10:00 am - 5:00 pm
About Conference The 5th World Congress on Cardiovascular Medicine Pharmacology, scheduled for July 24-25, 2025 in Paris, France, invites experts, researchers, and clinicians to explore [...]
Events on 2025-06-30
Events on 2025-07-10
AI in Healthcare Forum
10 Jul 25
New York
Events on 2025-07-21
Events on 2025-07-24
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.