Pediatric moonshot
A mission to create privacy-preserving, real-time applications based on access to data generated in all 1,000,000 healthcare machines in all 500 children’s hospitals
4 minute read
Translating AI research from the bench to bedside
Federated learning holds the key to unlocking much of the AI-in-medicine research work around the world and brings it from the bench to the bedside. But how?
3 minute read
A federated learning lab for children’s medicine
How can federated learning technology be harnessed and applied to training AI in children’s medicine applications?
4 minute read
Federated learning for consumer artificial intelligence applications
Federated learning is the key to training real-time, privacy-preserving artificial intelligence (AI) applications. So, what is federated learning?
4 minute read
Decentralized architectures for AI in medicine
To create real-time, privacy-preserving AI in medicine, applications require a new decentralized computing infrastructure
4 minute read
Will centralized architectures work for artificial intelligence in medicine?
Does the same centralized architecture approach, responsible for much of the progress in consumer artificial intelligence (AI), work for AI in medicine?
3 minute read
Why have we seen rapid advances in consumer AI?
How are AI applications able to recognize common images with greater accuracy than humans?
5 minute read
Why artificial intelligence for children’s medicine?
What are the challenges facing pediatric healthcare today, both in the US and around the world?
4 minute read