Clinical Trials for AI Models

You can—70% of hospital AI teams do. But shadow mode runs on live patients for a year and only finds failures after deployment, when fixes are expensive and programs get cut. We simulate 12 months of production chaos in weeks, catching missing data failures, edge cases, and population shifts before you deploy. Epic's sepsis model ran in shadow mode and still missed 2 out of 3 cases in production. We would have caught that before a single patient was impacted.

Not even close. Your test set has 0% missing data — production EHRs have 30%+ missing labs. Your test set is static — production involves EHR upgrades, sensor drift, and documentation errors. AUROC tells you aggregate performance on clean data. We tell you exactly when your model breaks: '30% missing labs drops sensitivity to 60%' or 'patients under 40 trigger miscalibration.' You need both. We provide the second.

Standard validation measures average performance on data similar to what the model trained on. We actively try to break it. We inject clinically impossible values (Heart Rate 600, Potassium 15) to verify the model flags nonsense rather than amplifying it. We test clinically identical patients — like monozygotic twins — to catch numerical instability. We simulate missing data patterns, EHR upgrades, and documentation artifacts that drive spurious predictions. The goal isn't to measure average performance. It's to find the specific scenario where your model breaks before a patient encounters it.

Yes — and it's a byproduct of stress-testing, not an add-on. FDA's January 2026 revised CDS guidance requires clinical AI to enable a clinician to independently review and understand the basis for a recommendation (Criterion 4 under 520(o)(1)(E)). The 2026 guidance's transparency requirements demand evidence on four properties: data provenance, representativeness, recency, and signal attribution. Every Krv stress-test produces structured output covering all four. The evidence is produced by testing — not written after the fact. A model that can't demonstrate these properties is likely classified as a medical device, triggering 510(k) or PMA requirements.

A No-Go is a diagnosis, not a dead end. When we find a failure mode — a demographic blind spot, a missing-data failure threshold, an unstable signal — we generate synthetic scenarios specifically designed to strengthen the model against that gap. This means synthetic data generation for underrepresented cohorts, guardrail design for edge cases, and iterative retesting until the failure mode is closed. What you certify today holds at deployment. And if clinical reality shifts, you can retest. The improvement loop is what separates us from validation approaches that tell you a model failed but leave you to figure out why and how to fix it.

You share your model and deployment context — sepsis prediction, readmission risk, ICU triage. We spend a week mapping your production environment: what data arrives when, what's typically missing, who your patient population is. Then three things happen: we stress-test your model through thousands of synthetic production scenarios; we produce a structured evidence package answering the four properties FDA's 2026 guidance requires you to demonstrate (data provenance, representativeness, recency, signal attribution); and if we find a failure mode, we fix it — targeted weight updates in the neighborhood of the gap, no full retraining, no data leaving your environment. You receive a Go/No-Go finding, a structured evidence package ready for regulatory review, and a model tested against production reality before a single patient is impacted.