Identify Clinically Meaningful AI Failure Modes
SCILabel supports safety-focused evaluation of healthcare AI using defined scenarios, expert review and structured analysis of model behavior.
Test the situations where model behavior matters
Safety evaluation should be designed around the intended system, user population, clinical context and plausible failure modes.
Scenario Testing
Create evaluation scenarios reflecting defined clinical and operational risks.
Harmful Output Review
Identify responses or predictions that could create clinically meaningful risk.
Hallucinations
Evaluate unsupported claims and fabricated information in generative systems.
Boundary Behavior
Test defined cases where the model should defer, abstain or communicate uncertainty.
Error Severity
Differentiate errors according to project-specific clinical significance.
Expert Analysis
Use appropriate domain reviewers to interpret observed failures.
Build your healthcare AI data workflow with SCILabel
Talk to our team about your healthcare data, annotation or AI evaluation requirements.