Quality & compliance · November 2026
Validating AI in a GxP environment
An AI model that evolves is hard to freeze, yet computerised system validation assumes a controlled system. How to reconcile the two.
The challenge
In a GxP environment (clinical trials, manufacturing, pharmacovigilance), every computerised system must be validated and kept under control. A machine-learning model can drift with the data, which means rethinking validation.
A risk-based approach
- Defined scope of use: what the model does, and what it does not do.
- Controlled data: quality, traceability and versioning of datasets.
- Performance criteria set before go-live, with continuous drift monitoring.
- Human oversight proportionate to the risk for the patient and for data quality.
- Change management: knowing when a model update requires revalidation.
Our view
Quality frameworks (GAMP 5, data integrity) remain the foundation. Authorities are working on AI-specific guidance: it is better to prepare by documenting now.
General information, not legal advice.