AIGR™ Health
Governance ratings for AI in clinical and health-system environments.
AIGR™ Health focuses on whether high-consequence healthcare AI is supported by traceable evidence for safety, validation, human oversight, privacy, supplier control and lifecycle monitoring.
Clinical safety
Intended use, escalation, harm scenarios and safety ownership.
Model validation
Performance, subgroup analysis, validation boundaries and change control.
Human oversight
Override, escalation, competency and accountable clinical decision rights.
Data & privacy
Lawful use, minimization, retention, deletion and access governance.
Suppliers
Supplier evidence, model dependencies, contractual controls and change notification.
Monitoring
Drift, incidents, safety signals, periodic review and retirement.
Clinical claims require clinical evidence.
Policies and vendor statements are supporting material, not substitutes for system-specific validation and operating evidence.
Performance evidence matches the intended population and use.
Human escalation paths are explicit and tested.
Safety and drift thresholds have owners and response actions.
Material model changes trigger new evidence and re-assessment.