And the experience doesn't show more data is better, it shows that an excess of features leads to overfitting.
People are proposing smart whatever now, and handeaving its value proposition as -eventually- feeding into ML which will -eventually- have benefits to patients.
I don’t think there is disagreement that data + ML is useful, but the value for increasing data because of AI assistance isn’t meaningful in the absence of that AI. Hospitals and insurers have huge data sets available: build a halfway decent AI first, and then argue for the value-add of the data.
Except any department in any hospital or insurer that welcomes you to do research can make that de-identified data available to you with a simple contract.
How do you think college freshmen get in the door to work on research with physicians?
No, it’s not public, but it’s trivially accessible.
But I did. No, I use the word “trivial” knowingly.
The data for insurers is in the form of Billings (which come with Diagnosis codes!), which follows a federally-standardized format (check out the Medicare website) and you can get a sample of 5% of Medicare subscribers’ data off the Medicare site without any hoops to jump through.
I want to say, “if you actually know what to do with it, PM me and we can talk details,” but the fact that you don’t already know about this sort of low-hanging fruit suggests you’re from outside the hc sector looking in.
AI is not going to "think" like humans. It is not going to be better or worse, just "different". And all experience so far show that the more data the better are the decisions made by AI.