Seeing someone actually build something like this, even if it's not perfect, gives me a sense of hope. When you combine domain expertise with some AI tools, you don’t have to wait around for someone else. You can just start.

The question could be: What images are most often mistaken? What characteristics do they share? Knowing the highest false negative images would be really valuable people to know what not to ignore.

I would love to see more of such classifiers for other medical conditions, googling for images tends not to produce a representative sample.

Namely: “I have an idea for an app but I don’t know how to do it and I don’t want to spend all my time and resources on the app either. The app is a means to an end, not the end itself.”

We are now living in a time where getting to the end is much more possible.

One or two seemed quite obvious to me as concerning or not but turned out to be the other way