Maybe I'm out of the loop, but I haven't seen anything demonstrating results on "data" – the kinds of challenges that are actually valuable to businesses.
Why is that? Are those just less sexy / more proprietary in nature, or is there something about those challenges that make NN's less useful to them?
Second, there is also the peer reviewing problem. You are still trying to explain a very abstract concept to your peers in a paper which is usually limited to 6 or 8 pages. Text and images make for very graspable examples in such a short paper. That's the reason why some other data with a spatial prior is not used as often, like time series or EEG-data.
So, there is a combination of those two elements at play.
The second reason is pretty bogus (text/images more graspable). It's valid if you're talking about mass media / popular press. But for research papers 1) images / large snippets of text are actually a negative since images take a lot of space and 2) the people doing peer review are expert scientists. They know the benchmarks and the theory.
Examples are the whole Predictive Maintenance sector, the medical sector (Computer Aided Diagnosis) or insurance companies which use NN to for all kind of analyses.
In fact I remember talking to an ML guy with a PHD who was working on one of these types of problems and I asked "why not try NNs on this problem". He looked at me with disgust and said something akin to "it's provable that NNs can never do better than BOOST, so why use them?"
However, boosted decision trees don't work on image analysis at all, so these types of problems have become the standard for NNs.
It is also worth noting that people are more likely to try image problems if everyone else is trying image problems, because then it is easy to compare multiple algorithms together.