Before a clinical model goes live, prove it won't fail quietly.
Pasteur stress-tests clinical predictive models against the conditions that break them in the real world — noisy vitals, missing labs, and populations they never trained on. Coming soon.
A model with a great AUROC can still be dangerous.
Validation today is a static 80/20 split on data that looks like training. Deployment isn't static — and the failure shows up in production, on a real patient.
Distribution shift
Performance on a held-out split is not performance on tomorrow's patients — the static metric never sees the cohort move.
Silent overconfidence
Models rarely say “I don't know.” They guess — confidently — on impossible inputs, and nothing in the AUROC flags it.
Rare cohorts vanish
Synthetic data generators drop rare phenotypes, so the model fails exactly where the stakes are highest.
We map where a model is safe to use — and where it isn't.
Pasteur simulates thousands of realistic clinical perturbations and traces how predictions deform under each, producing a map of the model's safe operating envelope. Its data-fidelity layer (TRAILED, a topological method) keeps synthetic trajectories clinically faithful — including the rare cohorts standard generators erase.
Under active development with a California pediatric hospital.
Built toward the bar regulators actually set: a clinician should be able to judge a recommendation without trusting your model. AUROC and SHAP plots don't clear it.
Pasteur is in active development.
We're working with early clinical and model-risk partners. If you're deploying clinical AI and need to know where it breaks before it does — let's talk.
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