To go further, a large issue/distraction in biomedical research has been Big Tech types coming in assuming that the roadblock all along has been lack of smart computational people in the process. I will not name names, but have seen so many instances of personal tech heroes coming in & claiming the underlying problem was that some of the brightest computational I've ever encountered simply didn't know how to computer.

Nothing could be further from the truth. People coming in to "disrupt" only add noise. Eventually those people either understand this and put the effort in to understand the domain or they wander off.

While it’s absolutely true that naive non-experts can end up adding a lot more heat than light, I’ve also seen non-expert people come into stagnant domains and absolutely completely transform and improve upon the state of the art.

I am skeptical of the “more data is bad” meme of screening hesitancy. It cannot be scientifically true in the strictest sense, and to the degree it’s an accurate assertion, it really seems to reveal an unscientificness to how screening data is used today in practice rather than that in principle more data is bad.

In a perfect world you're right. What it's getting at is that screening itself isn't very good in the grand scheme of things, and thus the negatives of extra screening can be argued to be worse than the extra screening. Whether or not that's true, well that's another matter.

The issue in this subthread was the notion that the only thing between the current state of affairs and high quality screening is a bit of disruption. The problem is hard, smart people are working on it, more smart people are always welcome, it'll still take a while.

It's not that more data is bad.

It's that 1) sometimes collecting the data itself is harmful at scale. E.g. mammography can cause breast cancer, or cause it to spread. 2) the patients actions as a result of the data can, and does at sufficient scale, cause further harm.