[cameronfraser]

It could be useful as a tool to help a radiologist do their job better though. I think many of these techniques described in ML papers will be used to enable people to be better at their jobs rather than replace them. At least until there is AGI at least.

[neuro_image3]

I wouldn't dispute this (if they finally put something together that isn't horrendously cumbersome, time-consuming and hard to use) but this doesn't justify the 'AI is about to replace radiology' crap I seam to see every time some academic group publishes an AI/ML paper.

[cameronfraser]

I don't think this paper makes that claim, you might be thinking of media interpretations of papers which is usually the one making bogus claims. It says that it outperforms 5 out of 5 people, but that is in this specific context. They aren't necessarily claims meant to be generalized that much.

[neuro_image3]

I see that type of hyperbolic claim several times in the comments.

I wasn't just commenting on the abstract presented. I was commenting on the comments I see here, as well as comments I see related to similar papers all the time.

I also interact with AI/ML researchers all the time. Most of them are typically some combination of: 1. Poorly informed about the appropriate context and utility of medical imaging. 2. Trying as hard as they can to push AI/ML as the most important technology in medicine today. 3. Pursuing a very task-specific project which they claim is massively generalizable in some (incorrect) way.

[StreamBright]

Usually they do not understand that your workflow if not revolving around pattern recognition on pictures. The best use of ML in pattern recognition for radiology is ordering images. You would get the the images sorted by the likeliness of having something unusual.

[StreamBright]

What do you think how far AGI is?

[cameronfraser]

a long long time

[StreamBright]

I guess over 100 years at the very least.