Clinical Trials for AI Models — preventing healthcare AI deployment disasters by rigorously stress-testing models before they reach patients.
70% of healthcare AI models fail to reach production because they break under real-world conditions. krv labs bridges the gap between offline validation and clinical reality by stress-testing models against the chaos of actual hospital environments—missing data, workflow shifts, and population drift.
The krv labs platform subjects clinical AI models to thousands of realistic scenarios to identify failure modes that traditional metrics miss:
- Resilience Testing: Simulating EHR outages, missing labs (30%+), and sensor drift to ensure graceful degradation.
- Stability Analysis: Verifying that minor, clinically insignificant data shifts don't flip critical predictions.
- Generalizability: Stress-testing models across diverse age groups, ethnicities, and comorbidity combinations.
- Sanity Checks: Injecting impossible data and logic errors to ensure models catch nonsense instead of amplifying it.
Traditional validation uses clean, static datasets. Real hospitals are messy. We help teams ship trustworthy models in weeks—not months—by pinpointing exactly where and why a model will break in production.
Backed by NVIDIA Inception, Berkeley SkyDeck, PAD-13, and TUM Venture Labs.