Scaling AI at the Edge of Healthcare

COVID-19 Thermal Screening Systems by Whiteboard Coordinator have been deployed to several of the nation’s top hospitals. The system has been used to maintain screening rates well over 2,000 healthcare workers per hour at individual check points. Most importantly, the system does so while adhering to levels of privacy above and beyond those required in the Health Insurance Portability and Accountability Act (HIPAA) and Biometric Information Privacy Act (BIPA) of Illinois. 

Delivering AI at the edge greatly reduces cybersecurity, data privacy, and latency issues to ensure a safer workplace and healthcare environment for all. Whiteboard Coordinator and NVIDIA are bringing AI to the edge of healthcare with NVIDIA Clara Guardian.

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A Business Model for Machine Learning in Healthcare

How federated learning will drive a new economy of privacy-centric algorithms in healthcare. How Federated Learning Ensures Patient Privacy In medical school, one of the foundational tenets we are taught to uphold in the practice of medicine is a patient’s right to privacy. When it comes to healthcare data, privacy is not a nice to have or a want to have, it is a fundamental human right. We may accept consumer technology companies’ core business of tracking our movements and desires across cyberspace in the course of providing us with targeted advertising, but that’s not an acceptable approach to healthcare data, and never will be. So how can healthcare leverage notoriously privacy-insensitive and data-hungry machine learning techniques like deep learning to transform our productivity at the same scale realized over the past decade in finance, industry, and consumer technology? Perhaps ironically, the solution to machine learning privacy in healthcare was first popularized by...

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