Afaik AGI research is still a separate thing, so it's not really that misleading to use "AI" for applications like this were machine learning is involved, resulting in a very specialized "artificial intelligence" that can spot otherwise hidden patterns.
Agreed about the sample size. It's not clear from the article whether the high accuracy was based on testing against the training set, too. I assume not, as that would be exceptionally sloppy. But it's not clear.
Of course, that does mean the results don't necessarily generalize to the whole population, which is why you do eventually need much larger sample sizes when you're working toward FDA approval.
In my experience it is far more likely to mean "doesn't generalize well, and moving to a reasonable training set will reveal flaws in the model.
That's definitely the case in conferences like O'Reilly Strata. They seem to be 100% focused on AI/ML, which seems really limiting. I'm starting to skip attendance until things get a bit more balanced.
In this case I guess the “AI” label is relatively well applied (compared to the scope of ALL things I’ve seen it haphazardly slapped on), but yeah, still - people need to stop calling any kind of data analysis “AI”.
Also, I've noticed the definition of the word "AI" has grown to encompass pretty much any type of software that does something with data.