The Validation Gap
Traditional CSV frameworks were designed for deterministic software. AI/ML systems — probabilistic outputs, continuous learning, limited interpretability — don't fit standard GAMP 5 categories cleanly.
A Risk-Based Framework
We classify AI/ML systems along two dimensions: GxP impact (patient safety, data integrity, business process) and model complexity (rule-based to generative AI). This matrix determines appropriate validation rigor.
Key Elements
- Intended Use Documentation with performance boundaries and failure modes
- Training Data Governance: provenance, quality, bias assessment
- Continuous Performance Monitoring replacing point-in-time validation
- Change Control for model retraining and updates
- Explainability Documentation appropriate to risk level
Regulatory Alignment
Aligns with EMA reflection paper on AI, Swissmedic guidance, and FDA's TPLC approach for AI/ML-based SaMD.