Laboratory AI Governance & Validation
Define intended use, risk controls and evidence for AI-assisted laboratory work. Governance guidance does not certify a model or establish regulatory compliance.
Technical evaluation & safeguards
- NIST AI RMF offers voluntary cross-sector risk management; use it as a framework, not a certification claim.
- Inventory model/provider versions, permissions, training-data use, vulnerabilities and change notifications.
- Apply risk-proportionate validation to intended use; test failure modes, human oversight, rollback, drift and vendor updates.
- Part 11 applicability, data integrity and computer-system validation depend on actual regulated use. FDA CSA guidance has a particular production/quality-system context, not universal drug-development applicability.
Prepare a buying or implementation brief
- Intended use, decision risk and applicable regulated records
- Validation ownership, de-identified challenge data and acceptance criteria
- Supplier assessment, incident response, model-change control and periodic review
A controlled supplier pilot
- Use authorized, de-identified data and a written intended use; do not send client or participant data without appropriate permission.
- Request a demonstration against your own acceptance criteria, including error cases and export/exit requirements.
- Agree integration, security, validation ownership, ongoing monitoring and model-update responsibilities.
- Laboratory Vortex coordinates inquiries, does not endorse products, validate your system or guarantee performance.