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There are far larger foundational problems in the healthcare system that prevent further digitalization. None of the points you name strikes me as a good use c

There are far larger foundational problems in the healthcare system that prevent further digitalization. None of the points you name strikes me as a good use case for modern machine learning techniques: - Siri and co can hardly understand everyday things. Using NLP for symptoms seems like a massive downgrade from speaking with a human. - how does machine learning play a role in pre-screening? Pre-screening can also increase costs because you find a lot of harmless things as well - I hope you will Put out a product telling if something is safe or not using machine learning. There are already systems out there and they are much more akin to experts systems which makes much more sense. The only area where I see a big impact is medical imaging where you can already see great research going on.

nopinsight 7y ago
Yes, I agree that current technologies in deployment are insufficient for many of the things described, especially language understanding. A few of my previous posts this year expressed skepticism of current tech in detail. I work in NLP/NLU and foresee some of future advances that will enable them. Moreover, BERT et al models that were just invented over the last 1-2 years may already allow the implementation of basic versions of these human language technologies. In many domains, they are on-par with humans in answering multiple-choice/span selection questions. See, for example: https://gluebenchmark.com/leaderboard https://gluebenchmark.com/leaderboard http://www.qizhexie.com/data/RACE_leaderboard.html http://www.qizhexie.com/data/RACE_leaderboard.html I also agree that technologies are not silver bullets. They are enablers/accelerators of improvement in process and efficiency that can increase human well-being.