AI Validation in Regulated Environments

Start with intended use and consequence

The assurance question is not “is it AI?” but “what decision does this influence, and what happens if it is wrong?” An assistant that drafts a summary a qualified person reviews is a different risk profile from a model that classifies a deviation or releases a result.

There is no universal FDA requirement that applies to every AI application in regulated industry. Expectations depend on context of use, the applicable predicate regulations, and whether the output affects product quality, patient or user safety, or regulated records. Where a model is itself part of a medical device, an entirely different regulatory pathway applies.

Controls worth establishing now

• Documented intended use, scope of use and explicit out-of-scope statements
• Risk assessment covering incorrect, biased, unstable or fabricated output
• Data provenance, quality and representativeness for training and prompting
• Human oversight proportionate to consequence, with reviewer accountability
• Performance criteria, acceptance thresholds and test data sets
• Change management for model versions, prompts, parameters and retraining
• Ongoing monitoring for drift, degradation and unexpected behavior
• Supplier assessment for third-party models and hosted services
• Record integrity: what is retained, how it is attributable and how it is reviewed

Where AI meets computerized system assurance

Most GxP AI applications sit inside an existing computerized system boundary. The pragmatic path is to extend the assurance framework already in place — intended use, risk, proportionate testing, lifecycle control — rather than to invent a parallel program with different vocabulary.