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Physician Burnout Symposium
2021-01-07 - 2021-01-29    
All Day
Physician and Nurse Leader burnout is a public health crisis that demands action across the entire healthcare ecosystem. Burnout not only affects clinicians, but also [...]
Annual World Dental Summit
2021-01-18 - 2021-01-19    
12:00 am
Dental World Conference will provide an international platform for discussion of present and future challenges in oral health, dental education, continuing education and expertise meeting. World-leading [...]
Nutrition & Food Sciences
2021-01-25 - 2021-01-26    
All Day
Meet Inspiring Speakers and Experts at our 3000+ Global Events with over 1000+ Conferences, 1000+ Symposiums and 1000+ Workshops on Medical, Pharma, Engineering, Science, Technology [...]
Environmental Toxicology and Pharmacology
2021-01-27 - 2021-01-28    
All Day
EnviTox webinar 2021 offers a unique online platform to present research work and know the latest updates with a complete approach to diverse areas of [...]
Events on 2021-01-07
Events on 2021-01-18
Events on 2021-01-25
Events on 2021-01-27
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.