AI VALIDATION PRACTICAL GUIDE
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What to Expect in Our Practical Guide to AI Validation
This guide shows how intended use, GxP criticality, model and data lifecycles, and monitoring can be integrated into a transparent validation approach.
- Intended Use and Risk Assessment from the Perspective of GxP and AI Regulations
- Requirements for Models, Data, and Data Governance
- The Role of Proof of Concept in Validation
- Continuous Monitoring, Guardrails, and Handling Drift
- Expanding the V-Model to Include AI-Specific Activities
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