The new infrastructure of healthcare AI
AI adoption in healthcare is life threateningly slow due to the siloed nature of real world hospital data, missing incentive alignment between data holders and technology vendors and lengthy legal approval procedures.
For the same reasons, existing machine learning models are extremely hard to deploy inside hospitals, which leads to millions of dollars in opportunity cost and thousands of lives not saved every day in the US alone.
We are building a federated network of hospitals, where the data never leaves the servers of participating institutes. Instead we bring the compute to the data.
Train and deploy
Train state of the art deep learning models on rich EMR and image data, then extract insights or deploy directly into the EMR systems of the participating hospitals.
If any data of a participating hospital is used to build a machine learning model, it will be remunerated accordingly and potentially on a continuous basis.
Please read our whitepaper to find out more about our approach, proposed revenue model and the NeoGlia team.
Get in touch
Let us know if you'd like to be part of our proof of concept work.