[gotrecruit]

my shallow knowledge of machine learning tells me that the idea behind it is that initially it will suck at diagnosis and analysis, but over time the algorithm will learn and improve to a point where eventually it becomes actually good at it and even exceeds human capability.

[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.

[hiddencost]

That's a dangerous way of thinking about ML. Models aren't magic, they're a approximate hacks that end up working for a specific instance of a problem.

More data is always nice, but typically you see accuracy level off (diminishing returns). ML is a constant process of improving your data, increasing the amount of available data (not the same as improving your data), improving your features, and improving your model. No one thing is sufficient.