Clinical performance is local
An AI system validated in one environment can meet a different reality in another. Local prevalence, scanner settings, workflow sequence, clinician behaviour and data completeness can all affect outcomes. That is why a clinical AI product needs more than an aggregate accuracy claim. It needs a way of seeing whether it remains useful in the environments where it is actually deployed.
Measurement becomes part of care infrastructure
The practical implication is an evidence loop. Inputs, outputs, overrides, downstream outcomes and incidents must be available for review without turning every clinical interaction into a research project. The challenge is to create monitoring that is meaningful enough to detect drift, light enough to operate continuously and transparent enough for clinicians and regulators to understand.
What a strong signal would look like
Manfred will treat prospective, workflow-level evidence as stronger than retrospective performance marketing. Useful signals include clear site-level validation, explicit calibration plans, disclosure of where a system should not be used and a credible process for incorporating post-market evidence. The durable advantage may sit with teams that can run this learning loop responsibly, not with the first team to show a compelling demonstration.