What happens between a clinical question and a published result.
A doctor brings the problem
Work starts with a clinician who wants part of their job faster, cheaper, or less subjective. We scope it first: would a working model change any decision?
We start before the data does
Collection and annotation at the hospital take months. We build on public data meanwhile, so a model is ready for the first site cases.
The dataset grows under the model
Site data first tests what public data taught the model, then joins the training set. It keeps growing with every batch.
Doctors review the real outputs
Once accuracy is reasonable, the clinicians who raised the question look at what the model actually produces. What they flag sets the next round of work.
We test where models break
Our clinical data comes from one hospital. Before claiming a model generalises, we run it on other centres and other scanners.
The part medical AI can't skip.
The Patrick J. McGovern Foundation.
The foundation funds AI built for public benefit worldwide. Their support is both a grant and a vote of confidence in where this work is headed.
