[john_b]

Machine learning, in this context supervised machine learning, is a useful tool for deriving unintuitive relationships between different parts of complex data sets. To do this, there must be some discernible correlation between the parameters of interest that isn't subsumed within the noise of the system+measuring device(s).

In this case, those parameters would be the image data and whatever health parameter is of interest (e.g. white blood cell count). My initial skepticism, perhaps that of the parent comments as well, has more to do with whether the measurements are of high enough quality for any reliable analysis to be done. The app doesn't seem to require any background or contextual data either (though I haven't verified this). If not, false positives and negatives could be problematic.

Anyway, machine learning isn't a form of magic that can transform data with no meaningful sensitivity to something into a something that is sensitive to it.