Let me make it simpler: How much blame was your company willing to absorb if your algorithm made a faulty diagnosis?
The importance here was demonstrating efficacy, which we did fantastically well.
Once we prove efficacy for multiple use cases, we can at least remove the "oh you computer scientists dont get it" argument and can have adult conversations about how to progress state of the art rather than continue to bleed patients dry.
I'll admit there are definitely barriers like what you mention. But those barriers are not some impenetrable force once we break down real issues and deal with them separately and start treating the problem as one we can solve as a society.
Totally agree. But science moves in baby steps and progress builds on progress. We started ML by doing linear regression. Then we moved onto recognizing digits. Then we moved onto recognizing cats. Suddenly, Google Photos can find a friend of mine from 1994 in images it appears to have automatically sucked up. That is amazing progress.
Similarly, our viewpoint as co-founders in the space was to solve a single use-case amazingly well and prove AUC and cost/value metrics. The field wont be moved by me or you, it will be moved by dozens of teams building upon each other.
No, you can't. Stating this is a clear proof that you don't understand what you're dealing with. In medical ML/AI, efficacy is not the issue. What you are detecting is not relevant. That's the issue. But I know I won't convince you.
So it does in fact work. Unfortunately, only in trivial cases.
We're terrible at bayesian logic. Especially when it comes to medical tests, and doctors are very guilty of this also, we ignore priors and take what should just be a Bayes factor as the final truth.
Why does it surprise you then that doctors and patients don't take the solution seriously? You don't have any skin in the game! Whereas the doctors are on the hook for malpractice, and as for the patient, well it's life or death for them.