
AI vs. MD: what happens when the diagnosis is automated - cft
http://www.newyorker.com/magazine/2017/04/03/ai-versus-md
======
Houshalter
It's worth noting that humans completely suck at almost any task even simple
statistics can be applied to. Even world class experts will typically be beat
by simple linear regression. See here for many examples:
[http://lesswrong.com/lw/3gv/statistical_prediction_rules_out...](http://lesswrong.com/lw/3gv/statistical_prediction_rules_outperform_expert/)
And this is very old research with methods that are very primitive compared to
modern machine learning (most of this was done on pencil and paper!):

>Wittman (1941) constructed an SPR that predicted the success of electroshock
therapy for patients more reliably than the medical or psychological staff.

>Carroll et. al. (1988) found an SPR that predicts criminal recidivism better
than expert criminologists.

>An SPR constructed by Goldberg (1968) did a better job of diagnosing patients
as neurotic or psychotic than did trained clinical psychologists.

It's completely amazing we allow human doctors to make diagnoses at all. At
the very least algorithms should always be part of the process (but note that
humans given the results of an algorithm still do worse than just the
algorithm on it's own.)

The problem is there has been enormous resistance to the use of algorithms in
medicine. People irrationally distrust algorithms and strongly prefer human
judgement. Even when they know the algorithms are superior. Psychologists have
actually studied this phenomenon and have named it "Algorithm Aversion":
[http://opim.wharton.upenn.edu/risk/library/WPAF201410-Algort...](http://opim.wharton.upenn.edu/risk/library/WPAF201410-AlgorthimAversion-
Dietvorst-Simmons-Massey.pdf) This isn't even getting into the institutional
resistance to change or having people lose their jobs to robots.

~~~
rayiner
It's interesting that you mention the criminology scenario. Courts are
starting to use software algorithms to predict recidivism rates for purposes
of setting bond amounts. The reports are even given to judges to inform
sentencing. However, there is an enormous backlash against them, because
_e.g._ they predict higher rates for racial minorities:
[https://www.propublica.org/article/machine-bias-risk-
assessm...](https://www.propublica.org/article/machine-bias-risk-assessments-
in-criminal-sentencing).

~~~
Houshalter
The propublica study has been shown to be a lie, see
[https://www.chrisstucchio.com/blog/2016/propublica_is_lying....](https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html)

Anyway all I'm claiming is that statistical methods are much more accurate
than humans. Nothing there disputes that claim. If you want the most accurate
predictions possible, you should use an algorithm.

That article implies that humans are somehow fair or unbiased. That is a
completely ridiculous claim that has been proven false many times. Human
judges give ugly people twice the sentences of attractive people. Judges have
been shown to give significantly harsher sentences just before lunch, when
they are hungry. Not to mention all the classic biases against
gender/race/political affiliation/etc. Studies have shown interviews are worse
than useless at assessing how good someone will be as an employee. Instead
employers are biased by how much they like the candidate. We should hardly
expect traditional parole interviews to be any different.

But almost no one cares about these results. Yet when an algorithm is shown to
have a (not statistically significant) bias, people freak out. This, if
anything, proves my point that algorithm aversion is a serious problem.

~~~
xkxx
> Human judges give ugly people twice the sentences of attractive people.
> Judges have been shown to give significantly harsher sentences just before
> lunch, when they are hungry.

It would be nice if you could provide sources for these claims.

~~~
soundwave106
The lunch statistic probably came from this study:
[http://www.economist.com/node/18557594](http://www.economist.com/node/18557594)
[http://www.pnas.org/content/108/17/6889](http://www.pnas.org/content/108/17/6889)

(To be fair, the study size was small).

There's a study on attractiveness and juror bias here (it's more complicated
than just "ugly people get worse sentences" but some bias does show up for
certain juror personality types):
[http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract](http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract)

~~~
sjg007
If there is ever a dress for success time it is when you are in court.

------
throw2away
Instead of reading the usual hacker news echo chamber, probably good to read
what doctors thought about the article too.

[https://www.reddit.com/r/medicine/comments/61sgfw/ai_versus_...](https://www.reddit.com/r/medicine/comments/61sgfw/ai_versus_md/)

~~~
Para2016
Thank you for posting this.

The article and some of the people quoted in it, like Hinton, seem to think
MDs need so much help for diagnosis. Real life isn't House MD. The answer is
very often obvious from the history/physical exam and the most basic labs.

Anyways, I don't like the fact that Hinton and others seem perfectly OK with
not knowing how the machine is diagnosing. It's machine clinical gestalt.

I also think this diagnosis by machine would be very frustrating. Imagine, as
a patient, asking the diagnostic robot: "why do you think this happened to
me?" or "Why do you think the diagnosis is x?"

And then not having an answer other than "the imaging and tests are consistent
with imaging and tests of previous patients with x disease" \- this sounds
like a bad answer. That wouldn't be good enough for me, but maybe it would be
for plenty of other non-curious parties. Maybe there is this huge group of
people who want healthcare from robots with robotic bedside manners. But I
doubt it. Hinton is wrong, doctors are going to be augmented by helpful
diagnostic applications. We will still have to learn to diagnose on our own
but we will have help too. Maybe a robot to help triage cases into
"serious/less serious" categories (and with working initial diagnosis) with
good accuracy.

~~~
tvanantwerp
To be fair, when I ask my doctors why I'm having XYZ issues, they usually give
me an unsatisfactory answer like "maybe your diet", "maybe you're stressed",
"hard to say exactly", or some slightly more tactful variant of "it's all in
your head". At least with a machine, I'd feel less looked-down-upon.

------
entee
There's a lot of interesting things going on at the intersection of medicine
and AI. That said, it's important to understand some of the limitations:

1.) Medical data is often quite sparse and quite poor quality

You may only get a few years here and there for a patient, and a lot of the
things mentioned in the article ("cough is raspy", "I have a feeling it might
be pneumonia") aren't always in the medical record, and even if they are they
aren't in a form that's easily accessible to a computer.

2.) Interaction matters

Seeking medical care is an extremely vulnerable state to be in. A good doctor
isn't doing just diagnosis, but teasing out the right bits of information.
It's unclear how a computer will handle, "I feel I'm not getting the full
story from this patient" situations. A good doctor (not all of them are) will
have the interpersonal skills to get the full story.

Finally, even if you solve diagnosis, then what? You have to take action. For
a lot of expensive chronic conditions, it's not like the answer isn't obvious
or even particularly needs diagnosis. If you're overweight, you probably
already know you should eat less and maybe be more active. Many times even
people with certifiable diabetes diagnoses do not change their lifestyle
appropriately. How you handle putting the diagnosis into action is a tough
problem, and it's not entirely clear how things like AI will fix it.
Convincing people to change habits is damn hard.

Disclosure: I work as a data scientist at a medical AI startup
(www.lumiata.com)

~~~
HillaryBriss
> Interaction matters

really, just following your lead here, but i guess we should then ask: do
patients respond better to a doctor they have a good relationship with?

i mean, there are some patients who might say, "Well, I made a deal with my
doctor that I'd reduce my sugar intake and try to keep my blood glucose levels
down below X, on average" (or whatever) and maybe they're motivated by a
desire to please their doctor or not disappoint their doctor.

but, could that work as well for a robot doctor?

what would happen if there were a robot doctor that patients liked _more_ than
any of their human doctors?

------
rscho
Tech communities hold a common opinion regarding ML and medicine: "bring
machines to the clinics! Research shows them to be superior". Everyone always
takes great care in forgetting that the ML research for medicine and diagnosis
assesses those systems in a well-defined setting. I.e, you have to feed the
system the appropriate info for it to work. From personal experience, I can
say that obtaining this info can be exceptionally difficult for a multitude of
reasons, and trying to fully automate that is currently not economically
doable. The medical system currently does not have the information-retrieval
capability to make ML really useful.

I am sorry to say that anyone thinking that we could "ML all the things" in a
hospital or office practice on this here day clearly has no idea what a mess
our hospitals really are. Some things are amenable to ML though, and most of
us welcome any help we can get. Even from machines.

------
dkarapetyan
Doctors are terrible. The sooner they're out of the diagnostic loop the
better. Every time I go to see one I get a different diagnosis or something
generic. It's basically a crapshoot based on the experiences of the doctor.

I'd much rather have a machine do all the pattern matching without any human
bias.

~~~
G2kyd7
The problem with human pattern recognition is not the accuracy itself. A big
problem is that 1. human experts are not consistent between themselves and
worse 2. not even consistent if they have to evaluate a case repeatedly. One
would expect an AI error to be systematic and not changing between
predictions. In the short term, an AI (as in Augmented Intelligence) approach
would be a good intermediate. Another advantage of AI diagnostics could be its
availability in smartphones, especially with skin cancer that could be useful
to quickly check marks and spots. Already doctors are advising people to self-
check for unusual marks etc., and usually a very big problem is that people
wait too long before seeing a doctor.

~~~
rpazyaquian
The problem with that last bit is that there's also the issue of convincing
doctors that you suspect something's wrong. Sometimes, doctors don't take
patients seriously when they're concerned about something, or don't make more
than a cursory glance at the situation before figuring that the patient is
overreacting or something. I've been dealing with some breath/lung related
problems for a few months now, and I still have to tell my doctors that yes, I
have issues breathing sometimes, and yes there's something going on.

Doctor-patient trust is a big issue, especially when the former doesn't take
the latter seriously.

------
guimarin
I care a lot about outcomes at the individual level. I worry that with these
systems, generally health will improve because there will be a level of
standardization, but specifically it will get worse. An analogy for me is with
furniture, but you could also substitute financial advice. In furniture we can
get a 'quality' table from Ikea for a couple hundred bucks, and we can make
10s of thousands of them. But it's much much harder to get a reasonably priced
custom table. 200 years ago when a lot more people spent time building wooden
ships, it was much easier to get a carpenter to build a table/furniture at a
relatively affordable price point. Now that comparatively orders of magnitude
of fewer people are in the practice, the price for that furniture, or custom
cabinets, etc. at the same quality level has skyrocketed. In financial advice,
the rise of algorithms doing things like asset allocation has caused an entire
generation of financial planners to never be hired, and therefore never
trained. The old financial planners who do much more than asset allocation are
becoming rarer and rarer and no one is replacing them. In 20 years, I firmly
believe the specifics of my tax situation, etc will be more difficult to solve
than today because I will have a harder time finding this kind of specialized
expertise. I see the same in medicine and every other field which low-end
machines doing the low-end work. We're not training the future high-level
practitioners because they need those 10,000 hours or whatever to become super
experts. How long will it take before the machine catches up with the best
humans is really the question here.

~~~
scott_s
> 200 years ago when a lot more people spent time building wooden ships, it
> was much easier to get a carpenter to build a table/furniture at a
> relatively affordable price point.

I'm going to make a call on that claim. Keep in mind that 200 years ago was
1817, well before the rise of the middle class. I bet that far fewer people
could afford the luxury of getting a table even approaching the quality of an
Ikea table, let alone what a few $100 extra will get you for a custom table
today.

~~~
rpazyaquian
The Industrial Revolution was sometime from 1760 to 1820, I thought?

~~~
scott_s
Yes. I realized the same thing a few minutes after posting, and edited that
part out. The middle class part is what matters most: I think more people now
have significantly more purchasing power than 200 years ago.

------
jamra
My Uncle worked in this field. He told me that when given suggestions from AI,
residents would make more incorrect diagnosises. I believe the theory was that
most of medicine is probability based, but sometimes you can see someone and
get a feeling that you may have a lower probability illness. However, when
receiving suggestions from the AI, residents were second guessing themselves
and performing worse.

I believe that in cancer research, ML will be crucial. Finding similarities
among mutations of cancerous cells. I am really hoping cancer treatment can
benefit from this.

------
kendallpark
My not-yet-expert* response whenever my classmates joke about me building some
AI robot-doc to replace them:

"AI will become just another tool in a physician's toolkit."

AI will not replace the role of a doctor. However, the role of a doctor might
shift to overseeing the AI in very specific scenarios. Furthermore, I'll add
that there's a lot more to medicine than making diagnoses.

As it stands right now we have no limit of promising applications of AI in
healthcare. The bigger issue is getting these applications into the clinic.
Very few of these boundless applications are actually implemented in a real-
life setting that can affect patient outcomes. I can't tell you how many
studies are published proclaiming, "HEY AI CAN DO X BETTER THAN DOCS." Cool
study, bro. Now can you actually get that into clinic and start saving lives?

Suchi Saria at JHU is one of the few people I know that has bridged that gap.
Other resources for those that are interested:

\- [http://mucmd.org/](http://mucmd.org/) \- Baxt, William G. "Application of
artificial neural networks to clinical medicine." The Lancet 346.8983 (1995):
1135-1138.

*Programmer MD student about to start my PhD in comp sci specifically machine learning + healthcare.

------
faragon
20 years ago, as student, I took Expert Systems (rule-based) and
"Bioinformatics" (neural networks, backpropagation stuff, now called "deep
learning") courses, and it was like Expert Systems based on rules -with or
without fuzzy logic- would be the future. While neural networks were in the
same "box" as other techniques like genetic algorithms (evaluation function
driven partially random guesses, with greedy/non-greedy "evolution", etc.).
Back then it was like imminent being able to put the knowledge of an expert
e.g. a medicine doctor so the artificial expert system could make decisions as
good as the one made by the human. After years, it seems that human experts in
some areas were not so easy to replace, not because not being able to mimic
the rules related to medical diagnosis, but because the inability to filter
"bullshit-input" (creative hypochondriac people, multiple patterns recognized
at once, etc.).

The "deep learning" (backpropagation stuff) reminds me the euphoria of the
expert systems in the nineties, but with palpable results, i.e. despite
requiring crazy amounts of computer power, you can see some difficult problems
solved. What scares me is that instead of the "clean and understandable"
modeling of the rule-based expert systems, the "deep learning" (neural
networks backpropagation stuff) is hardly understandable, even by experts
(learn how to train a model is one thing, and knowing why it works, is another
-e.g. you can correlate success for N training cases, guess that you're
covering the model, and then, discovering for the N+1 that there was a
correlation/causality issue -e.g. discovering you trained for learning blue
things instead of square things, just because the square things were blue, and
when a red square appears, it does not get identified-).

------
jepper
These articles are always way overhyped. A lot more development is needed
before it can even be safely used as a tool alongside radiologists. The
dermatology example is an excellent one. It would be great to have a simple
image classifier that can help the diagnosis before the clinical appointment.

Software on its own can't even tackle a 'simple' task as reliable 6 lead EKG
(electric heart film) analysis yet. A chest x-ray has a lot more variables.
Plus clinical variables as patient history etc can make a big difference in
diagnosis on a similar image.

~~~
ska
But there have been successful commercial applications of ML/AI working
alongside radiologists safely as a tool for at least a couple decades.

With all the current interest in "AI" it's easy to forget that this is an old
problem area, and current techniques don't fundamentally change anything. In
most applications the biggest issues remain access to and quality of the data.
For the right application though, you can do useful things.

------
SubiculumCode
As a layman, it seems to me that the biggest advantage that humans have over
machine learning is flexibility.

While machine learning performance frequently rivals or exceeds humans at many
individual tasks once sufficiently constructed and trained, only humans excel
at dynamically choosing which tasks to pursue, switching levels of analysis,
and when to break the rules for the win (losing at GO? unplug the computer).

To speculate heavily, animal based cognition may be composed of just such a
multitude of specialized trained modules, akin to machine learning algos of
today: object recognition, emotion recognition, language recognition, typical
script/structure of a given scenario, etc. But above that will be classifiers
that interpret internal and external environmental signals to choose which of
those specialized modules to engage and suppress. In lower animals lacking a
heavily recurrent prefrontal cortex, the higher order modules are probably
directed by mid brain structures to engage basic fight flight fuck behaviors
needed for survival (e.g. pattern recognition module sees snake, freeze or run
modules are engaged). In animals with prefrontal cortex, goal and context
driven suppression of prepotent responses becomes possible.

Anyway, it seems to me that for machine learning to become a general
intelligence, there will need to hierarchies of specialized machine learning
classifiers, some specialized in sensory classification, but others in that
are meta..classifying those classifiers into scripts, scenarios, etc.

------
lpgauth
It's funny to see how the HN crowd reacts to this. If AI was a magical bullet,
don't you think doctors would already be replaced by machines? Also, most
people don't think rationally when it comes to diseases... You might be able
to self-diagnose using google, but that's not something most people can do
without ending up thinking they have cancer.

~~~
taneq
There's a huge gap between demonstrating an effective algorithm and bringing
an appliance to market which uses that algorithm. The vast majority of
development work on any product goes, not into the basic science, but into
making it simple, user-friendly, reliable and safe. This goes tenfold when
you're talking about anything related to health or safety.

------
hyperion2010
I wonder how long it will take machines to get good at the 2nd part mentioned
in the article, the 20 questions part where they try to figure out causes
using only the conversation. For neurological disorders you also need an agent
that can actually do a manipulative exam to figure out what is wrong. Machines
are still far from having the full spectrum of agency needed to preform exams
to find the underlying source of a problem. Of course it is extremely rare to
find a human being who can do those as well...

~~~
CabSauce
[https://www.buoyhealth.com/visit/](https://www.buoyhealth.com/visit/)

There's some pretty good, interesting work in this area.

------
theoneone
Good luck to AI with procedures that require a doctor to search/find/diagnose
a small(0.4cm) breast tumor in a ultrasound exam that could actually save the
patient's life. AI can be applied(it is actually) in computer tomography scans
but in exams like X-ray, ultrasound etc you cannot make it work( at least
now). Also there is legal issue: who is responsible for the diagnose? The
computer? The institution ? A doctor? AI in medicine will help us a lot but
it's too early!

~~~
TeMPOraL
> _but in exams like X-ray, ultrasound etc you cannot make it work( at least
> now)_

Why not? An image is an image.

~~~
theoneone
Believe me it's not the same, especially at ultrasound where everything it's
not clear due external factors an body types. I do this for a living
(radiologist)plus I love programming and would like to see those two come
together but the reality is different. Ai could assist doctors and suggest
possible findings.

------
cs2818
I find it odd that the article seems to downplay the potential benefits
machine learning provides by emphasizing a lack of explanatory power from
classification algorithms.

At the very least it would seem that a machine-based classifier provides human
physicians and researchers with more examples to base their inquiries on
(possibly even illuminating some features they may have previously missed as
important portions of theoretical models).

~~~
randcraw
But more opinions (from AIs) that differ from the advice of the 'responsible
health professional' will surely add confusion to the doctor's life, and in
the US medical space, legal liability. In general, doctors are understandably
reluctant to invite more oversight, especially if it doesn't clearly add value
and if it's not independently and certifiably trustworthy.

Past AI apps, like 1980s expert-systems, generally relied on brittle binary
criteria that were hard to match with certainty. Too often they produced
results that were either obvious or implausible, but at least they could
explain themselves. They were also poor at matching against fuzzy clues from
patients (and doctors) who are notoriously inconsistent and nonquantitative at
describing symptoms. No doubt a greater emphasis on quantitation lies at the
heart of today's AI systems. But if the classifications and recommendations of
tomorrow's AIs lack explicability, there's no way in hell they'll be trusted
or given authority by risk-averse practitioners.

A middle ground is needed, where the 'advice' from the AI is grounded in clear
statistically significant bases and adds value to the process, rather than
competing with humans. In some spaces like suggesting cancer therapies, that
are more likely to succeed using quanta, I think AI will be adopted and
appreciated first. Primary care medicine will probably see it last, though it
probably is already employed invisibly behind the scenes by insurers for
validation and quality control (like prescription drug contraindication).

------
sebringj
I'm pretty confident the role of a doctor will be relegated to the role of a
calculator at some point. Nobody will do it anymore, rather, medical research
will be the focus and providing data to AI, then less skilled/cheaper work can
be done for the human care portion. IMO it is abuse to have someone work into
their 30's to eventually get paid after all the sacrifice and long hours.

~~~
TeMPOraL
The way many countries treat their doctors is indeed pretty abusive, but
that's orthogonal to the fact that effective medical care right now requires
_a shit ton_ of training. This is not artificial complexity. I hope one day
soon machines will surpass MDs and we'll all have access to an autodoc, but
until then - and since you brought abuse up - I think it's worth considering
what conditions we create for medical care people to work in.

------
techwizrd
The start-up I work for, GYANT[0], is actually working on this problem. The
author of the article, Siddhartha Mukherjee, also wrote The Emperor of All
Maladies: A Biography of Cancer, which was awarded the 2011 Pulitzer Prize for
General Non-Fiction. The book is very good, and so is this article.

0: [http://gyant.com/](http://gyant.com/)

------
hidden_sheepman
after reading this article it made me think about and episode of Scrubs I was
watching the other day, where Dr. Cox made a decision that led to 3 deaths of
his patients and after he was afraid to make decisions during the episode. I
thought to myself it must be difficult to make a decisions that could affect
peoples lives, would having a machine help make those decisions easier an more
accurate?

I mean sure you have doctors that have 20 years of experience but still get
the diagnosis wrong even it if it's close, but still it seems that compared to
machines that get feed large amounts of data still come up short to. I think
saying machines will replace doctors is the wrong approach, in the article one
of the doctors interviewed said "If it helps me make decisions with greater
accuracy, I’d welcome it”. Thats we need more tools that enable doctors to
make more accurate decision than going on an experienced hunch.

I think it's great this subject is being explored it will help more people,
and doctors do their jobs even better.

------
quickben
Our family doctor jokes that people come lately with: oh I googled what's
wrong with me, I'm here just for a second opinion. :)

~~~
theprotocol
I find it's a good sign that he jokes about it. I've encountered many doctors
who were bitter and insecure about patients who researched their symptoms
online, and reacted with a knee jerk, passive aggressive "you're a
hypochondriac" prejudice that pervaded the diagnostic process.

~~~
balabaster
It's definitely a good sign that he jokes about it, on the other hand, I would
prefer a doctor that acts as a consultant, there to give proper advice.

When I go to the doctor, I have Googled the symptoms I'm experiencing before I
go. Once I've been given a diagnosis by the doctor, I Google the diagnosis to
verify that I have the symptoms one would expect from such a diagnosis.

Having had and heard too many experiences where Doctors got it wrong to huge
detrimental effects, I want to double check what I've been told and not just
blindly accept what one Doctor has judged in 30 seconds based on an initial
perception of me without really knowing anything about me.

I think if I've correlated what I thought was a possibility along with what
the Doctor has diagnosed and the expected symptoms of that diagnosis, at least
I can be confident to a degree that I can trust the diagnosis, prognosis and
the course of action provided.

If I am experiencing symptoms vastly different than I would expect from the
diagnosis, I want to be asking questions as to how and why the doctor feels
they are correct and I am incorrect. I realize I'm not a doctor, but in this
day and age, for us all to have the world's information at our fingertips, to
be blindly believing anyone whose advice could have catastrophic consequences
on our health and lifespan is shortsighted at best and plane idiocy at worst.

That doesn't make someone a hypochondriac, that makes someone cautious about
misdiagnosis. Unfortunately, there are many hypochondriacs out there.

~~~
theprotocol
I have the exact same views as you on this. I've also experienced many, many
years of consistent medical malpractice, to deleterious, life-ruining effects,
so I had no choice but to take my health into my own hands and make rigorous
determinations as to whether the doctors I would see were right or wrong.

While most people may start off as hypochondriacs (who hasn't been spooked by
the prospect of cancer after reading about it), the more research you do, the
more accurate you become and in recent years I've finally managed to become
effective at discerning good doctors vs bad ones.

~~~
balabaster
I never tended towards hypochondria. I do however have trust issues given the
malpractice that I've witnessed over the years. I thank my lucky stars that
I've never been directly on the receiving end of it. I'm firmly of the belief
that we should treat our medical professionals as consultants and advisors,
but we should be fully cognizant of our own health requirements.

------
drchiu
Don't be ridiculous.

Machines can't replace doctors. You can't sue a machine for chance occurrence
poor medical outcome.

------
onuralp
Amusing trivia from the article: Geoffrey Hinton is the great-great-grandson
of George Boole.

