I'm going to agree this isn't over fitting. And it's dangerous to imply over fitting is ever a good thing.
If your objective function is to ID one type of Batman, you just have a very specific objective function which could also suffer from over fitting (i.e. it's unable to perform well against out of sample Batman of the same type).
The reason I push back is that many healthcare models are micromodels by your definition: they may only seek to perform well on images from one machine at a single provider location on a single outcome, but they also have over fitting issues from time to time since the training data isn't diverse enough for the hyper specific objective.
If your objective function is to ID one type of Batman, you just have a very specific objective function which could also suffer from over fitting (i.e. it's unable to perform well against out of sample Batman of the same type).
The reason I push back is that many healthcare models are micromodels by your definition: they may only seek to perform well on images from one machine at a single provider location on a single outcome, but they also have over fitting issues from time to time since the training data isn't diverse enough for the hyper specific objective.