The main critique for CheXNet I've read was focused on the NIH dataset itself, not the model. The model generalizes quite well across multiple visual domains, given proper augmentation.
Except they don't. See the table in the original post. Also, comparing the "average" radiologist by F1 scores from a single experiment (as you've done in other comments here) is meaningless.
Unless my doctor is exactly average (and isn't incorporating additional information, or smart enough to be optimizing for false positive/negative rates relative to cost), comparison to average statistics is academic. But I don't really need to tell you this -- your comment has so many caveats that you're clearly already aware of the limits of the method.
On one hand, we have one commenter saying he can train a model to do a specific thing with a specific quantitative metric, to demonstrate how deep learning can incredibly powerful/useful.
On the other hand, we have another commenter saying "But this won't replace my doctor!" and therefore deep learning is overhyped.
The two sides aren't even talking about the same thing.
That kind of hyperventilating stuff is easy to brush off. The problem with deep-learning hype is that comments like "my classifier gets a ROC/AUC score of 0.8 with barely any work!" are presented as meaningful. The difference between a 0.8 AUC and a usable medical technology means that most of the work is ahead of you.
So I think it's come down to conflict between
1. Which the author is trying to present 2. What an astute reader might interpret it as 3. What an astute reader might worry an uninformed reader might interpret it as
And my feeling is that, given all the talk about hype in pop-sci, we're actually on point 3 now, even when the author and reader are actually talking about something reasonable. Whereas personally I'm more interested in the research and interpretations from experts, which I find tend to be not so problematic.
Just to get back to this point: what if the vision system of your doctor is below average and you augment her by giving her a statistically better vision system, while allowing her to use the additional sources as she sees fit. Wouldn't be that an improvement? We are talking about vision subsystem here, not the whole "reasoning package" human doctors posses.
https://stanfordmlgroup.github.io/competitions/mura/
On just about every test set, the model is beaten by radiologists. Even the mean performance is underwhelming.
https://stanfordmlgroup.github.io/projects/chexnet/
In their paper they even used "weaker" DenseNet-121 instead of DenseNet-169 for Mura/bones. DenseNet-BC I tried is another refinement of the same approach.
Basically, I see this as "everyone sucks, but the AI maybe sucks a little less than the worst of our radiologists, on average"
Some people mention Matthews correlation coefficients, Youden's J statistic, Cohen's kappa etc. but I haven't seen them in any Deep Learning paper so far and I bet they have large blindspots as well.
There is also higher chance that next state-of-art model would push it significantly over 83% or best human radiologist at some point in the future, so it might not be very economical to train humans to become even better (i.e. dedicate your life to focus on radiology diagnostics only).