Clinical Trials for AI Models

You can, and 70% of hospital AI teams do exactly that before their models fail anyway. Shadow mode runs your model on live patients for a year, but it only catches problems after deployment—when it's too expensive to fix and executives are ready to kill the program. We simulate 12 months of production chaos in 5 weeks, finding missing data failures, edge cases, and population shifts before you deploy. Case in point: Epic's sepsis model ran in shadow mode and still missed 2 out of 3 sepsis 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 balanced across demographics. Production ICU patients cluster in ways you didn't train for. 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 'Asian patients age 65+ trigger miscalibration.' You need both. We provide the second.

Standard validation uses held-out test sets—which are still clean, complete, and static. We don't just test your model on different data. We simulate production chaos: missing labs, delayed vitals, EHR outages, sensor drift, documentation errors, staffing constraints. We inject impossible data (Heart Rate 600) to test your model's sanity checks. We test clinically identical patients (like twins) to ensure stable predictions. We simulate population shifts your test set never covered. This isn't more validation—it's adversarial stress-testing against realistic clinical scenarios your model will actually face.

You upload your model and describe the deployment context (sepsis prediction? readmission risk? ICU triage?). We spend 1 week mapping your EHR data patterns—what arrives when, what's commonly missing, what your patient population looks like. Then we generate thousands of realistic clinical scenarios: missing labs, delayed vitals, EHR outages, edge-case patients. We run your model through all of them. 1 week later, you get a report: 'Passes Generalizability and Sanity tests. Fails Resilience test—30% missing labs drops sensitivity to 60%. Recommend retraining with missingness-aware features.' Then we spend 3 weeks iterating and improving your models so they're production ready. Total time: 5 weeks. Total cost: $50K-$150K. Beats 12 months and $200-500K for shadow mode that finds problems too late.