
A.I. vs. M.D. - merrier
http://www.newyorker.com/magazine/2017/04/03/ai-versus-md?currentPage=all
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sosa2k
I read the book Complications by Atul Gawande a while back and it touched on
this issue. He mentioned how a computer was more accurate at detecting heart
attacks than an experience doctor. He also talked about how if the computer is
better than the doctor at reading things like these, it really doesn't make
much sense for the doctor to have to evaluate/approve the results. Kind of
reminds me of James Simons' thought process on quantitative trading at
RenTech.

Sorry about the lack of technical knowledge into the CS stuff, first post here
and I haven't really put in the time to learn about CS and AI yet.

~~~
dr_zoidberg
> Sorry about the lack of technical knowledge into the CS stuff, first post
> here and I haven't really put in the time to learn about CS and AI yet.

No need to apologize, you made an interesting and relevant comment. The fact
that you feel the need to apologize is more telling of the culture you
perceive in this site than of your lack of knowledge in any area.

~~~
sosa2k
I posted this comment about 15 minutes after I discovered Hacker News. Wasn't
sure on the culture here compared to places like reddit. It seems to be
similar to the more specialized subreddits.

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rrherr
I recommend reading "Do machines actually beat doctors?" by Dr. Luke Oakden-
Rayner, who is both a radiology doctor and deep learning researcher.

[https://lukeoakdenrayner.wordpress.com/2016/11/27/do-
compute...](https://lukeoakdenrayner.wordpress.com/2016/11/27/do-computers-
already-outperform-doctors/)

~~~
lukeor
Hey, thanks for liking the article. I was just looking at my blog stats and
they had a bit of spike when you wrote this.

I'm just getting down to writing some posts about the big question the New
Yorker article introduces, but doesn't really make any real progress with:
will machines actually replace doctors.

Hope you enjoy them :)

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mhb
Previous discussion:
[https://news.ycombinator.com/item?id=13976357](https://news.ycombinator.com/item?id=13976357)

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shiven
A few decades from now, 30 - 50 years at most, doctors and patients, will look
back at us and wonder, why we were _not_ using algorithms to assist with
diagnosis already.

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Nadya
As with many things - fear that the AI will be wrong (no matter how less
frequently this happens than with doctors) and a desire for a _human_ to blame
if there is a mistake. If a doctor makes a misdiagnosis and kills the patient
as a result it is less bad than if an AI makes a misdiagnosis and kills the
patient as a result. It's the one thing I don't understand about most of
society.

Same with self driving cars. Self driving cars could be proven to be 100,000%
safer than human drivers - but until it is legally mandated people will prefer
humans behind the wheel because "what if the self driving car runs a red light
and kills someone?" ignoring the hundreds and thousands of humans who run red
lights and kill people.

 _> why we were not using algorithms to assist with diagnosis already._

On the bright side - we increasingly are! I think it's more an issue with
budgeting and legal issues that it isn't as widespread.

~~~
a_imho
Apples and oranges. We do have driving assistance, fully autonomous vehicles
are entirely different. Low hanging fruits are being picked, not wanting a
software to call the shots every time is a valid stand imo.

~~~
ubernostrum
I think aviation is a good analogy.

Most of the automation in a modern airliner, for example, isn't there to make
the plane "fly itself" from point A to point B -- it's there to do things
machines are good at in order to reduce the workload of the human crew, so
they have more time/energy to focus on the things humans are good at.

Flight plans, for example, are still decided on by humans, even if a
programmed computer carries out many aspects of the plan once decided upon.
Same for en-route deviation from the prior plan in order to either avoid bad
weather or reach good weather (where "weather" is kind of a broad term, and
includes things like winds which would help/hinder an airliner). Although
"autoland" is a feature, it's not something that's used every time and can be
moderately complex to set up, since a bunch of factors have to be
decided/input by a human. And of course the final decision to land or go
around is made by a human.

Which drives home the fact that automation, in aviation, is a _partnership_
between humans and machines, with humans benefiting and being more productive
from offloading some work.

A lot of fields should be looking at that as a model.

~~~
adrianN
And yet there are cases where humans interfering with the automation caused a
crash.

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ubernostrum
Yes, humans refusing to trust or work with the automation can cause a
disaster. That's not an argument for going fully automated or fully human,
it's an argument for developing better cooperation between the humans and the
automation.

~~~
tragomaskhalos
And in many cases it has been poor UI design preventing the humans from
recognising or understanding what the machine is telling them, particularly
when they are under the extreme stress of an undiagnosed emergency situation.

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digitalzombie
Hopefully more companies are doing this.

My research thesis is in this area, hoping for a nice career doing modelling
to predict illness. I'd like to have a nice paying job doing something that
actually help people instead of just only making somebody more richer.

Also it doesn't matter if the AI gets wrong, the algorithm gives say 80%
accuracy and it tells you base on your genetic make up if you should take the
surgery route or chemotherapy route.

1\. It's to assist the doctor. Also perhaps it can be cheaper to diagnose than
a doctor. If say the algorithm says with 80% chance you have cancer, then you
should go to your doctor and have it check. If it says no, then don't go. You
have another tool to evaluate your health keeping in mind that it's a tool and
aid, not a replacement for doctor.

The only concerns are genetic discrimination which GINA law addresses. And
medical algorithm usually err on the side of false positive. So it rather get
it wrong in saying you have cancer than saying you don't have cancer and in
reality you do.

Any body know of any companies that does this please send them my way. ^__^

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TuringNYC
I am doing this, full-time for almost three years soon. The company is at
[http://dochuddle.com/](http://dochuddle.com/)

So here is the warning: Medical startups are hard, really hard. \- It is
difficult to apply research on 32x32 cat icons on 15megapixel xrays. \- It is
difficult to get data without year-long contract efforts \- It is very
difficult/expensive, if not impossible, to use cloud resources due to HIPAA
and localization rules so you need to build your own on-premise GPU grids like
we did \- It is difficult doing most medical things in the USA unless you are
on the revenue side (e.g., collections, increasing yield, etc.) -- we got so
many raw deal partnership offers in the US that we went overseas to trail our
product.

The entire medical system in the US is corrupt from the ground up, geared to
maximize revenue with minimal lawsuit risk. Patient care rarely enters the
conversation internally. I thought financial services was bad (my past
career), but at least the metrics were all agreed upon by everyone. In
medicine, everyone has an agenda, often diametrically opposed to other
parties.

~~~
bilbo0s
Keep in mind that TuringNYC did not even MENTION the main issue that trips up
most startups...

ie - they are unfamiliar with how to secure, (and KEEP), FDA approval for
their products. It actually comes as a surprise to many of them that they even
NEED FDA Approval. Then they are further shocked by how invasive the process
is, and how long it can take. And don't even get me started about the look on
their faces when they realize that, despite all of that, you're still held
CRIMINALLY accountable for any bugs in your software. (Stay away from anything
that might injure anyone if you are unsure of the ability of your process to
reliably prevent bugs. Things like RTP are no-go areas for all but the most
meticulous medical software developers.)

Don't get into medical software unless you have an extremely long, and very
well funded, runway. This is not an industry where 2 guys in a garage can
innovate and side-step the regulations. And whatever you do, stay away from
anything that could actually harm the patient if it is wrong.

~~~
TuringNYC
Very good point @bilbo0s and thank you for bringing this up. I didn't mention
it as we focused on a non-clinical diagnostic (thus not subject to FDA),
decided to entirely avoid the US market (focus on more less risk averse
economies open to innovation and aligned via ACO-style systems.)

FDA is definitely the biggest hurdle of them all. I'd shudder if we had to
deal with that 600 pound gorilla.

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StJuice
if you give a flying (sic) about solving medical issues, and actually know a
thing or 7 about curing disease, check out Kaggle's competitions. The cervical
cancer competition is of particular interest.

If you happen to disagree, step away from your doubt for a sec and listen: we
can, and will cure these diseases with AI. That's the whole point. We imbue
our intelligence into a machine and voila, the machine does what we ask it to
do with greater expediency and more acumen than an individual can do alone. We
don't garden with machines and say" wow, this took fifteen thousand people to
build, should we use it so we can do other stuff instead?".

Nope. We say, thank you John Deere, I'll take 2. While we are at it, let's
look a little deeper and think about how civilization functions in general. Is
that not what we do? We connect, we decide to work together, and next thing
you know, we improve our quality of life: otherwise known as a corporation (or
conglomerate if you wannanother version, ya heard?).

So, is AI good for medicine, yes: it is.

Here's a free thought to prove my point. Using my intellect today I deduced
that anxiety is absurdity masked as truth. Imbue that into some AI, you'll
heal the minds of the world, ok?

Peas in the ground, head in the air, thoughts with the 1. -Love

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dhruvp
I think it's really useful to see the MD's view of such changes here:
[https://www.reddit.com/r/medicine/comments/61sgfw/ai_versus_...](https://www.reddit.com/r/medicine/comments/61sgfw/ai_versus_md/).
Obviously not necessarily representative of how all MDs think but it's
interesting to see some fears/concerns they have.

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vonnik
Automating diagnosis goes too far. Deep learning will largely be used for
decision support; i.e. surfacing and indicating possible diagnoses for a
medical professional to choose from.

Most machine-learning companies have misunderstood the role of radiologists.
Their first job is not diagnosis. It is to find the right domain, the right
view of the data, from which to formulate a diagnosis. So the problem is to
map from one set of parameters, say, how an X-ray is taken, to another more
promising set of parameters for the same X-ray, to get the view they need.
It's not just see an image, find a tumor.

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Vkkan2016
I have read about khosla paper pretty much covered
[http://www.khoslaventures.com/20-percent-doctor-included-
spe...](http://www.khoslaventures.com/20-percent-doctor-included-speculations-
and-musings-of-a-technology-optimist)

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amelius
Is there any data openly available for developing this kind of system?

~~~
matt4077
Kaggle has a few datasets, including I believe mammographs and proteomes if
you want to do breast cancer specifically.

If you're not in it for the money, or if just fame is enough, I'd suggest to
go upriver: individual diagnostic data is rare because of medical privacy and
market forces, but there is boundless open data in, for example genomics and
proteomics. If you search for [bioinformatics competition], you should find a
nice selection of opportunities with good data availability and clearly
defined objectives. ML is slowly revolutionising this field, although it's a
good idea to pay attention to what happened before–there were some seriously
smart people working on these problems for a long time, and they found some
rather ways to extract the most value from data with the tools available at
the time.

