What to validate
Error, consistency and out-of-context use
Validation assesses responses, sources, stability, behavior in ambiguous cases, data failure, bias, interface, logs and operational impact.
Validation
Clinical AI validation before deployment
An AI solution in healthcare should not enter real routine use without testing for safety, consistency, limits and specialist review.
Clinical AI validation with testing for error, consistency, safety, documentation, human review, audit and post deployment monitoring.
How to document
Limits, human review and audit trail
Documentation must record what was tested, what failed, who reviews, when the system should not respond and how incidents are handled.
Monitoramento
Validation does not end at go-live
After deployment, the solution needs indicators, auditing, feedback collection, periodic review and change control.