[ska]

Stationarity isn't really the issue here, as you don't typically analyse this data as measurements from a single stochastic process.

However - you have hit on a very real problem. Imaging systems have got better over time, imaging quality even on the nominally same system can be different from different sites. Image coverage can change by both policy and system capabilities, etc.

It's worse the more sophisticated the imaging systems are. Consider MRI, which is perhaps better thought of as equipment to perform physics experiments than as an imaging device. In that case, nominally equivalent scans from different vendors (even different generation from the same vendor) can have significantly different characteristics. And there is a ton of processing going on, there is no such thing as "raw" data here - even the vendors themselves may no longer be able to really (or at least easily) characterize what is being done.

So yes, in any machine learning applied to these data sets, you have a very real risk of learning odd characteristics of the sample data and hurting your generalization.

Biology isn't as likely to be a problem I think, but biological response to changing treatment protocols, sure.