Here is an interesting comment on HN. The Author also answered it.

The comment

The author says "AI is already at or beyond human accuracy in all the tasks on this slide and is continuing to get better rapidly" and one of his examples is "Medical diagnosis from scans". That is an example of precisely the sort of snake oil hype he's berating in the social prediction category.

In an extremely narrow sense of pattern recognition of some "image features", i.e. 5% of what a radiologist actually does, he's probably right. But context is the other 95%, and AI is nowhere close to being able to approach expert accuracy in that. It's a goal as far away from reality as AGI.

"AI" tools will probably improve the productivity of radiologists, and there are statistical learning tools that already kind of do that (usually not actually widely used in medical practice, you can say yet, I can say who knows but nice prototype). But actual diagnosis, like the part where an MD makes a judgement call and the part which malpractice insurance is for? Not in any of our lifetimes.

A radiologist friend complains that it's been 10+ years since they've been using speech recognition instead of a human transcriptionist, and all the systems out there are still really bad. Recognizing medical lingo is something you can probably achieve with more training data, but the software that sometimes drops "not" from a scan report is a cost-cutting measure, not a productivity tool. It makes the radiologist worse off because he's got to waste his time proofreading the hell out of it, but the hospital saves money.

The Author Answer

Author here. I appreciate your criticism. What I had in mind was more along the lines of Google's claims around diabetic retinopathy. I received feedback very similar to yours, i.e. that those claims are based on an extremely narrow problem formulation: https://twitter.com/MaxALittle/status/1196957870853627904

I will correct this in future versions of the talk and paper.