
Predicting Medical AI in 2017 - brandonb
https://lukeoakdenrayner.wordpress.com/2016/12/31/predicting-medical-ai-in-2017/
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aub3bhat
I am a researcher in Medical / Healthcare AI, and currently developing a Caffe
like framework for learning from publicly available large claims dataset.[1]

I actually have a very pessimistic outlook. I think outside of few specific
applications in Radiology and maybe pathology, AI/ML is unlikely to succeed in
Medicine. Even Deep Mind which started its health division by building a Non-
AI app. The major issue in medicine is figuring out the right kind of
problem/intervention/interface.

Worse a lot of research in this field (outside of radiology) is pure hype. It
typically involves researchers teaming up with their friends in university
associated hospital to publish studies on private datasets which can never be
reproduced. Why worry whether the problem definition is correct or clinically
meaningful when you can claim anything and the community lets you get away
with calling your work Deepest Patient or AI Doctor. This is why Physicians &
medical researcher mock machine learning in healthcare research.

It is going to take another half a decade or even a decade untill unform
EMR/EHR datasets become available. And some Imagenet style competition weeds
out the current crop of GRU-LSTM-RNN-Stacked-Autoencode-GAE papers whose goal
is to stuff resumes with "hot" keywords rather than actually improve the state
of the art in an emiprical manner.

[1]
[https://github.com/AKSHAYUBHAT/ComputationalHealthcare](https://github.com/AKSHAYUBHAT/ComputationalHealthcare)

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kem
Thanks for the links--I wasn't aware of these resources.

I do research in a neighboring domain, and have sort of dabbled in these sorts
of datasets before, with a tiny bit of ML-ish research on different topics.
It's an area I've thought about expanding into.

My sense is that the sorts of EMR/EHR records you're linking to aren't fine-
grained enough for the most part to be useful for the sorts of things that
will constitute AI's presence in healthcare. I think they are incredibly,
incredibly useful, and will find a place in AI research (I've edited papers
using these sorts of datasets with AI), but my sense is that they're not the
sort of thing that will give AI a foothold.

I think your sentiment and that of the OP is right in that there isn't likely
to be anything major this year, but I think AI/ML will have a bigger presence
over the next 10 years than people are currently expecting. There's too much
research showing that many tasks currently done by clinicians can be done
better algorithmically by a machine, and in contrast to a lot of fields, the
content matter is _supposed_ to be approached that way--quantitatively and
scientifically.

What is needed is a lot of very fine-grained data. Not visit data or event
summaries, diagnoses, procedures, providers, but actual specific image files,
etc. and so forth. My experience is that this stuff is in EMRs sometimes, but
not consistently, and is never made publicly available. Some of the data--much
of it maybe--is not actually in hospital records at all. It might be sitting
in R&D or university labs somewhere, or isn't being collected at all.

My guess is that big corporations will start collecting this data one way or
another, or researchers will establish consortiums, or apps will start
amassing it as they gain traction, and that's how this will proceed.

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a-guest
RE: "What is needed is a lot of very fine-grained data. Not visit data or
event summaries, diagnoses, procedures, providers, but actual specific image
files, etc. and so forth." Interesting; can you offer other examples of "very
fine-grained data" you are thinking of, besides image files?

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brandonb
For those interested in AI in medicine, the most recent NIPS held a workshop
on exactly that topic: [https://nipsml4hc.ws](https://nipsml4hc.ws)

There's quite a bit of active research applying LSTMs, CNNs, and the like to
medical data sets. I'd say about two thirds of the work I saw was from
academia, but there are active efforts from industry: Google Brain, Empatica,
Cardiogram (us), and Evidation were all in attendance or presenting work in
progress.

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masthead
The link is refusing to connect. Can you update the link?

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brandonb
I can't edit my post anymore, but as of right now, the link works over http
rather than https: [http://www.nipsml4hc.ws/](http://www.nipsml4hc.ws/)

