[HN Gopher] Medicine's Machine Learning Problem
___________________________________________________________________
Medicine's Machine Learning Problem
Author : happy-go-lucky
Score : 59 points
Date : 2021-01-05 19:16 UTC (3 days ago)
(HTM) web link (bostonreview.net)
(TXT) w3m dump (bostonreview.net)
| hobom wrote:
| This includes some true things about data collection issues, but
| I cannot agree with the main thesis that ML algorithms are about
| power. If anything, they shift power to the patients because now
| the decisions can be checked and questioned at many levels. An
| algorithm will not send you away because it is to tired too take
| your complaints seriously.
|
| So, using algorithmic decision making should be as bias free as
| possible, but there is no way that across the board they will be
| more biased than humans are now. If people care about
| marginalized communities, they should push with everything they
| got for, not oppose, ML decision making.
| daginnbotty wrote:
| Question about this: they talk about how datasets are not
| representative, my question is, compared to what?
|
| I'm guessing by the politic of this article the person is from
| the US, do they want people to look at data representative of US
| population? That seems pretty narrow-minded.
|
| My country has almost no black people (probably <1% though I
| don't know that official stats on race are even collected) and it
| would be extremely expensive for us to include black people at US
| levels (13%) in all research.
|
| Perhaps they are advocating we use world demographics but that
| would be logistically impossible for basically all researchers
| around the world.
|
| Am I misunderstanding the author or does the article seem pretty
| ethnocentric? (is that the right word?) US-centric?
|
| Wouldn't it be better to just qualify/label the demographics of
| the research data (we used all black people, all white people
| etc). They talk as if there is some golden ratio we should all be
| following but that just isn't the case.
|
| In any case, I don't think it is useful to shame researchers that
| are just doing the best they can with the data available cause
| the data they have is useful to someone.
| BadInformatics wrote:
| For better or worse, most of these articles are US-centric
| because that's where most of the R&D money for health ML is.
|
| The far more reasonable approach is to make sure your data
| contains as many demographics as possible (not just race) from
| your actual patient population. If there happen to be gaps,
| then put in at least a reasonable effort to fill them instead
| of shrugging and saying "it's out of our control". That, along
| with a per-demographic breakdown of important metrics and your
| point about qualifying demographics in the data (which is
| already done in most medical publications, including many using
| ML) would already be a huge improvement on what most people do
| now.
|
| Ironically, it's the big tech companies that have the hardest
| time with this because they want to make generally deployable
| projects, yet don't have access to as much data as many
| healthcare orgs do. Frankly, I don't have much sympathy for
| them: a lot of this is trust issues from self-inflicted damage.
| jjcon wrote:
| > make sure your data contains as many demographics as
| possible (not just race) from your actual patient population
|
| By this you mean researchers should mirror their patient
| population as closely as possible (be it socioeconomic,
| gender, race etc) in whatever region they may operate in
| (which may not scale perfectly worldwide but will serve
| patients in that region well)?
| DoreenMichele wrote:
| "The first rule of any technology used in a business is that
| automation applied to an efficient operation will magnify the
| efficiency. The second is that automation applied to an
| inefficient operation will magnify the inefficiency." -- Bill
| Gates
|
| In the lifetime of my adult sons the world stopped being
| predominantly agrarian and rural and hit a point where more than
| half of all people on the planet live in cities. It wasn't hugely
| long ago -- a hundred or two hundred years -- that most people
| lived in little villages or tribes and didn't travel all the far
| and knew most people they dealt with.
|
| The local doctor -- or _medicine man_ -- was often one of the
| older, best educated and wisest locals. He tended to come to your
| home with a little black bag and in the course of walking through
| your house to check on you if you were ailing and not able to get
| out of bed, he saw a great many things about your life without
| having to ask.
|
| This informed his conclusions about what was wrong and about how
| to treat it. And it did so in a way that was largely invisible to
| the recipients of care.
|
| Doctors likely often didn't explain that the house was filthy or
| the spouse was obviously abusive. Topics like that tend to be
| socially unacceptable and people don't like being criticized in
| that way, but if someone smarter and more experienced and better
| educated and wiser walks through your life and then prescribes
| something "for your health" and he has a track record of fixing
| the problem, you do as you are told because you were told it.
|
| And then modern medicine invented a lot of diagnostics and what
| not and office visits by the patient replaced home visits because
| we haven't invented a Tricorder that can replace a little black
| bag and let you bring all that diagnostic power with you.
|
| Human health is no longer treated like the logical outcome of all
| your life choices and your physician is no longer the wisest
| person you know giving you good advice that takes into account a
| great many factors you never talked about with him. People get
| treated like specimens in a petri dish in a way that implicitly
| denies the fact that their physical state of health is the sum
| total of all their life choices.
|
| In tribal cultures, medicine men were typically people who tended
| to both spiritual and physical health. The two were not viewed as
| separate from each other.
|
| Medicine has become commercialized in a way that doesn't really
| serve the interests of the patient and if you try to point that
| out you are likely to be written off as some paranoid fruitcake
| and conspiracy theorist.
|
| There are a lot of good things about modern medicine, but there
| are also a lot of systemic issues and this article is correct to
| point out that AI tends to magnify those sorts of things.
|
| Last, health is best understood as a moving target in 4D. Data
| capture does a poor job of approaching it that way and I'm not
| aware of any programs that are well equipped to do a good job
| with that.
|
| Human doctors were historically put on call for up to 24 hours at
| a time as part of their learning process in part so they would
| see a patient's condition evolve over time while the doctor was
| still young and healthy enough to endure this grueling process.
| Having seen it for a time as part of their training, they
| retained that knowledge when they were older and could recognize
| a stage of a moving target.
|
| I don't know how much that is still done, but I don't think we
| really frame AI in that way. I don't know how we would get there
| from here either. I still haven't managed to learn to code, what
| with being too busy with my own health issues all these years.
| HPsquared wrote:
| It's a lot like the emergence of scientific forestry as
| described in "Seeing Like a State" - instead of local knowledge
| and care/attention to individual circumstances by a generalist,
| the field has become standardised and based around things which
| can be easily measured.
| [deleted]
| chromatin wrote:
| > Human doctors were historically put on call for up to 24
| hours at a time as part of their learning process in part so
| they would see a patient's condition evolve over time while the
| doctor was still young and healthy enough to endure this
| grueling process. Having seen it for a time as part of their
| training, they retained that knowledge when they were older and
| could recognize a stage of a moving target.
|
| Closer to 36 hours at a time, I am sad to report
| gdebel wrote:
| I'm an eye surgeon and self-taught machine learning practitioner,
| I started to learn Python in 2016 when the deep learning hype was
| at his highest.
|
| After 3 years of research, playing with datasets, extracting and
| cleaning data from EMR and from different machines, I not sure
| that the biggest problem with the so-called "AI" is the
| inequalities that it can induce ; it is rather, is it useful at
| all ? This is a little bit provocative so let me explain.
|
| First, it took me a very long time to really, fully get that AI
| is not fundamentally different from a simple linear regression
| (infering a rule from data). More powerful, but definitely, no
| intelligence added. Most of my fellow doctor colleagues still
| think that AI is special, different, like magic; I also thought
| like this before learning to code.
|
| Scores inferred from data were used in medicine from decades and
| fundamentally, nothing changed with the AI wave. I'm extremely
| impressed with the performances of GAN for image generation, and
| with what allows deep RL in controlled environments (which the
| human body is not): however, I can't see any applications of
| those technologies in medicine.
|
| Ok, Deep Learning allows to analyze images with a great level of
| performances. However, at the end of the day, nobody (informed)
| wants an AI diagnosis and the doctor will read the images. He
| will maybe have a pre-completed report : wow, incredible. We are
| very far from the disappearance of radiologists that Geoffrey
| Hinton took for granted a few years ago.
|
| At this time, a team published in Nature a paper about a DL
| algorithm which could diagnose melanomas better than
| dermatologists, using a picture. Unfortunately, no real-life
| application. Why? Because when you suspect a melanoma, if you
| have any doubt, you won't take a chance: you will take a biopsy.
| What is the interest of guessing the result of the biopsy that
| you will do anyway, because if you guessed wrong, the patient
| dies? No interest.
|
| I also realized that it is extremely difficult, if not
| impossible, to use data from EMR out of the box. Medical data is
| dirty, intrinsically, because humans are complex things that do
| not fit easily in little boxes. Hence, if you want quality data,
| you have to think your data collection in advance, and motivate
| all your fellow doctors to check the little boxes correctly. For
| many years (we are talking about big data, no ?) Of course, there
| is some exceptions, but most of the time the data cleaning
| process is extremely hard to perform (however, if a dedicated
| team of people with medical knowledge is concentrated on this
| work, things could be different. I had to clean the data myself).
|
| I'll finish with the most ironic part : I dedicated a few years
| of my life to a topic where both optics and prediction from data
| are involved (intraocular lens calculation in cataract surgery).
| I tried a great deal of ML approaches, only to find recently that
| by better accounting for the optical specificities of the problem
| I was trying to solve, I obtained excellent results, better than
| with ML, even with a dumb multiple regression. Ouch. The lesson
| is : physics beats AI.
|
| I would be happy to be challenged on this topic.
| refurb wrote:
| _First, it took me a very long time to really, fully get that
| AI is not fundamentally different from a simple linear
| regression (infering a rule from data)._
|
| I had a similar revelation. I sat through an AI for health
| presentation and basically asked "ok, so you take a data set
| then try and find a set of rules that accurately
| describes...like a linear regression?"
|
| As you said, it's more sophisticated than that, but in essence,
| yes, it's fitting a curve to data.
| DebtDeflation wrote:
| A big part of the problem is the names we've chosen -
| Artificial Intelligence and Machine Learning. A more
| accurate, though less sexy, name would have been
| "Mathematical Pattern Recognition". We can do amazing things
| with classifiers but we shouldn't fool ourselves into
| thinking it represents "intelligence".
| HPsquared wrote:
| Brains and intelligence are pretty much just pattern
| recognition as well - "neurons that fire together wire
| together"
| qayxc wrote:
| Brains in isolation, yes.
|
| But intelligence isn't just a "brain in a jar" situation.
| Intelligence requires interaction with the environment -
| you'd never be able to tell whether a Boltzmann Brain is
| intelligent from just observing it, for example.
| qayxc wrote:
| IMHO what's still lacking is the feedback loop.
|
| Current systems are limited to ingesting input and
| providing output.
|
| A big part of medical diagnosis, however, is to do follow-
| up exploration based on results of a previous examination.
|
| This is a big part of "intelligence" that's still missing
| entirely from all approaches that I'm aware of, i.e. the
| ability to ask further questions/request data based on
| preliminary results from previous inputs.
| matheusmoreira wrote:
| > I also realized that it is extremely difficult, if not
| impossible, to use data from EMR out of the box.
|
| This is my biggest complaint with the EMR systems I've used and
| I've always wanted to improve this. I wonder if fellow doctors
| would be okay with using a simple structured language to
| describe data in an EMR.
|
| For example: Height: 175 cm Weight: 70 kg
| Ethnicity: white Age: 40 years Creatinine: 0.9
| mg/dl
|
| An inference engine could use that data to calculate lots of
| things. Simple stuff like body mass index and creatinine
| clearance. The patient could be automatically classified in all
| possible scores given available data.
|
| Doctors already do this work, we even input this exact same
| data into calculator apps. The innovation would be recognizing
| this data in the EMR text and doing it automatically. I think
| it would be a huge gain.
| activatedgeek wrote:
| > I tried a great deal of ML approaches, only to find recently
| that by better accounting for the optical specificities of the
| problem I was trying to solve
|
| I want to point out, that any serious machine learning
| researcher is not oblivious to this, despite the deep learning
| boom suggesting to the contrary. Modern methods have shown that
| we are capable of building predictors with surprisingly complex
| representations, that can solve large-scale downstream tasks.
| i.e. our models are "flexible" enough.
|
| The next challenge is whether they favor the "right kind" of
| solutions. For instance, Convolutional Neural Networks (CNNs)
| are architecturally just sparse version of Fully-Connected
| Neural Networks. Why is it then that CNNs perform far better on
| images? A key reason is that "inductive biases" afforded by MLP
| aren't strongly favored towards images. Another instance of
| this is the covariance functions used is Gaussian Processes -
| the Squared Exponential Kernel is very flexible and can in
| principle fit anything possible. Nevertheless, if the problem
| has specific structures, say periodicity, one better use the
| Periodic Kernel because it's inductive biases rightly align
| with the kind of solutions we expect.
|
| > The lesson is : physics beats AI.
|
| As a consequence, the single biggest reason physics would beat
| a generic AI in the short-term is precisely due to our ability
| to explicitly provide inductive biases that align with our
| expectations from the physical system.
|
| We haven't found the secret sauce for every possible system in
| the universe. I don't think we can, either. But what we can do
| is devise ways to "control" such inductive biases we can encode
| in machine learning systems, which align with our expectations
| of the way the system should behave.
| n3ur0n wrote:
| I do respect your experience and take on the matter, however,
| let's replace this statement:
|
| "I'm an eye surgeon and self-taught machine learning
| practitioner, I started to learn Python in 2016 when the deep
| learning hype was at his highest."
|
| with:
|
| I'm a [machine learning researcher] and self-taught
| [ophthalmologist], I started to learn [ophthalmology] in 2016
| when the [clinical medicine] hype was at his highest.
|
| In this hypothetical situation, I bet you would instantly
| discount what I would have to say about ophthalmology because I
| clearly would not have the depth or experience to have an
| informed opinion on ophthalmology.
|
| Over the past few years with the ML hype, I have noticed quite
| a few clinicians who have self taught some deep learning
| methods claim expertise in the subject area (not targeting you,
| a general observation). I feel like many clinicians do not
| understand the breadth of machine learning approaches. There is
| just so much to know! from robust statistics, non-parametric
| methods, to kernel methods. Deep learning and deep generative
| models are by no means the only tools at our disposal.
|
| I absolutely agree with you though. Applied machine learning
| practitioners have been over selling their accomplishments --
| which I believe is detrimental to progress in the field.
|
| I would highly encourage you to collaborate with ML researchers
| who have spent a decade or more working on hard problems. From
| the other side, I can tell you I gained a lot discussing ideas
| with domain experts (neurologists, radiologists, functional
| neurosurgeons). They have insights that I could never have
| picked up by self teaching.
| gdebel wrote:
| Haha, you are perfectly right. I totally admit that I'm an
| amateur with a low level of ML expertise.
|
| One the other hand, ML researchers with a deep knowledge
| expertise are extremely hard to find, even among
| statisticians / programmers. I suppose that the people with a
| real expertise are working on their own startup or in FAANG.
|
| This leads to a situation where the medical research
| involving ML is largely without interest or full of bias. It
| is easy to spot in the literature.
| n3ur0n wrote:
| I think it's partly the incentive structure that is to be
| blamed. Historically, quantitative PhDs in
| healthcare(medical physicists, statisticians, comp.
| genetics) have been underpaid (in my opinion). Now with
| FAANG and Quant Funds willing to pay $400K+ comp packages
| to these PhDs, there are far more exit opportunities for
| these PhDs.
|
| On a positive note, I'm so glad that clinicians are taking
| interest in ML! As a practicing ophthalmologist, the fact
| that you were able to self teach is really impressive! I do
| know that a lot companies are looking for people like you,
| who have clinical experience. If you are interested you
| should explore roles/potential collaborations with some of
| these health research teams in tech.
| rscho wrote:
| The troubles we are seeing with medical AI integration are
| not stemming from lack of personal abilities, though. The
| problem is clearly systemic, with medical data being
| currently mostly unusable (for both humans and machines,
| although humans often believe otherwise). So you can be as
| good as you want either in medicine or ML or both, material
| support is lacking for wide applicability of medical AI.
| blueblisters wrote:
| This is an interesting perspective. Since you're an eye
| surgeon, this might be a relevant question.
|
| What do you think of the relative success of Diabetic
| Retinopathy (DR) diagnostic models, especially the FDA approval
| of the clinical trials Digital Diagnostics (formerly IDxDR)
| [1]? Their approach to the model architecture was slightly
| different from the black-box approach of other labs, wherein
| IDxDR's model is trained to look for clinically relevant
| indicators for DR. Is that a more likely route for future
| diagnostic AI models?
|
| [1]: https://dxs.ai/newsroom/pivotal-trial-results-behind-the-
| fda...
| gdebel wrote:
| Honestly, I don't know what to think. Ophthalmology is a
| great field for AI researchers (lots of images: the eye is an
| organ that you can photograph and analyze visually in every
| angles, almost like in dermatology). In Ophthalmology,
| diabetic retinopathy is an evident take : lots of people
| involved, lots of annotated pictures available, screening
| programs.
|
| However, I would like to see the performances of the
| algorithm on different fundus camera. It is also important to
| realize that diabetic retinopathy classification is very easy
| to learn, to the point that if the screening is such a
| problem, it is easier to ask the person that takes the
| pictures (In France, a nurse or an orthoptist) to phone the
| doctor when he/she sees something strange on the eye fundus.
| 5440 wrote:
| Also https://www.eyenuk.com/us-en/products/eyeart/
| rscho wrote:
| Not OP, but anesthesiologist and hobby programmer for 15
| years. What you are describing is a fundamental flaw of the
| current AI effort: the data that supports AI models is mostly
| irrelevant to the problem. In medicine, the saying goes: 90%
| of diagnoses are made on patient history. Ironically, there
| is no reason that would change for AI-enabled systems given
| the same information.
|
| So to answer you directly yes, it's a better route until we
| have better information available. But it's also the wrong
| route to take in the long term. It would be far better to
| attempt to produce better supporting information.
| sungam wrote:
| I am a dermatologist, AI researcher and co-founder of an AI
| startup (skinsmart.ai) and I would agree with you regarding the
| utility of AI in making an accurate diagnosis of melanoma. I
| don't think it has a significant role it play in the
| Dermatology clinic for this application. However, I am very
| optimistic about the potential for AI to help in the triage of
| patients referred to dermatology by non-specialists. For this
| application you are not trying to diagnose melanoma but instead
| aiming to diagnose - with a high degree of accuracy benign
| lesions that do not need review in the Dermatology clinic.
| sjg007 wrote:
| If I had a benign lesion referred by my PCP to dermatology,
| I'd want a dermatologist to take a look at it. It's never
| been difficult to get a dermatology appointment.
| sungam wrote:
| Situation may be slightly different in the NHS (national
| health service) where there is an overwhelming number of
| referrals from general practitioners for suspected skin
| cancer most of which turn out to be benign. As a
| consequence there is lack of capacity to see patients with
| other skin conditions. Of course it's always possible to
| see a private Dermatologist if you have health insurance or
| are happy to pay.
| sjg007 wrote:
| If that's true that sounds like a different problem.
| Maybe they need to train more dermatologists? And if
| there are appointments available privately well... I
| don't know what to say. Seems like an structural systemic
| failure which is odd. Maybe dermatologists are gaming the
| system to induce private pay..
| sungam wrote:
| The number of Dermatologists trained in the UK is
| entirely decided (and paid for) by central government. UK
| Dermatologists have for many years highlighted the need
| for training of more consultants.
| sjg007 wrote:
| Sure but the US has similar restrictions and problems.
| Medicare DME pays for almost all of the residency spots.
| In 2015 there were 400 dermatology spots in the USA. I
| guess one issue is travel time. A lot of folks don't live
| near cities and have access to specialty care.
| YeGoblynQueenne wrote:
| Is it necessary to go so far as making a diagnosis at all?
| Wouldn't it suffice to detect -and alert the user- that some
| of her moles have changed shape and she might need to have
| them looked at more carefully by an expert? This is a task
| that is very difficult to perform with the naked eye,
| especially for people with skin types that have lots of moles
| and an automated decision that could be relied on to detect
| otherwise imperceptible changes, could perhaps even save some
| lives.
| sungam wrote:
| Yes this is the idea of mole mapping and there are 3D whole
| body photo imaging systems available for this with
| automated detection of changing lesions. It's harder to do
| on a smart phone but maybe possible.
| YeGoblynQueenne wrote:
| Thanks - I'll have a look at "mole mapping" now that I
| know the term.
| ps2fats wrote:
| May I suggest, in response to your sentiment that applications
| of AI to medicine are lacking, is that you are seeing
| applications replace current medical practices. An AI diagnosis
| of a medical image seems redundant indeed, however in this
| situation a patient has seen a doctor out of complaints and has
| been sent to the radiologist for further investigation. This
| medical practice is reactionary, and suspicions are already
| present, so of course the AI isn't doing much useful here.
|
| Alternatively, imagine a proactive medical world, in which
| preventative screenings are commonplace. Currently, the
| implementation of routine screenings without any complaints is
| prohibitively expensive on a large scale. This is because it
| requires manpower, and manpower is prohibitively expensive and
| the expense of manhours needs to be justified by a medical
| practitioner. However, AI can help in this proactive medical
| world by reducing the number of hours real people are looking
| through data to detect problems of patients, reducing the cost
| of routine screenings at large. Again, this wouldn't replace
| doctors, as you'd still need a specialist to analyze any
| positive hits, but it differs from your scenario in which the
| AI diagnosis seems redundant.
|
| So, when preventative medical practices are more prevalent, the
| mass routine screening procedures will need help from machines
| to keep it cost effective, and that I believe is where this
| technology will find its application.
| twic wrote:
| Sounds good, doesn't it? But you have to have a really,
| really low false positive rate for this to work out. This is
| already a problem with mammography:
|
| https://www.cochrane.dk/news/new-study-finds-breast-
| cancer-s...
| dnautics wrote:
| I disagree. You don't really want to routinize that level of
| medical surveillance, due to the classical Bayesian
| predictive power problem. When you come in with a complaint,
| it changes the prior and is additional evidence to revise the
| diagnosis on top of the screening information.
|
| What you do want out of AI is to flag areas of interest in
| imaging for example and help identify when records are at
| risk of being incorrectly normalized. Ideally, even if the
| end effect is marginal (say bumping accuracy from 80% to
| 90%), if it enables a workflow that decreases the exhaustion
| and frustration of the doctor you will want that in place.
|
| Of course it could just as well be used as an excuse by
| management to increase any given doctor's throughput, so it
| might not work as you would want.
| dontreact wrote:
| Screening has been shown to be effective for lung cancer.
| With enough data, we can improve the posterior enough for
| certain applications that we don't need the stronger prior
| of complaints.
|
| Over time as AI improves, more and more diagnoses can look
| like this.
| rahulnair23 wrote:
| Health policy is fraught with counter-intuitive phenomenon -
| and screening is one of them.
|
| Seems like it should help, but in practice leads to over-
| diagnosis.
|
| For example - Cancer rates jumped in Korea after screening
| with no impact on patient outcomes [1]. There are several
| others.
|
| [1] Lee, J. H., & Shin, S. W. (2014). Overdiagnosis and
| screening for thyroid cancer in Korea. The Lancet, 384(9957),
| 1848.
| dontreact wrote:
| This is a false blanket statement. Also one that could
| change as we start to see human+ai performance be better
| than just human performance.
|
| For lung cancer screening, NLST showed a 20% reduction in
| mortality and now NELSON has shown even stronger results in
| Europe.
|
| This "all screening is bad" is FUD in the medical field,
| frankly. Yes it has to be studied and implemented
| carefully, but to make blanket statements about screening
| as a whole is factually incorrect.
| rahulnair23 wrote:
| I have not stated "all screening is bad".
|
| Broad-based population screenings as the parent comment
| suggests, in my opinion, are.
|
| I'm yet to see any clinically-valid distinguishing
| aspects that would suggest AI would add value to
| screening. Curious to hear evidence that drives your
| optimism of human+AI.
|
| Just to state, the NELSON study [1] focuses on high-risk
| segments. Their paper also recommends a "personalized
| risk-based approach" to screening. This seems reasonable.
|
| [1] https://www.nejm.org/doi/full/10.1056/nejmoa1911793
| dontreact wrote:
| The general thread here is about AI helping with a more
| proactive approach to medicine. Screening for high risk
| populations certainly falls under that.
|
| You certainly said that screening leads to over
| diagnosis.
|
| I think for screening, the best results are probably the
| upcoming prospective study from Kheiron.
|
| https://www.kheironmed.com/news/press-release-new-
| results-sh...
| dontreact wrote:
| I suspect, btw, that the Google model in this paper
| https://www.nature.com/articles/s41586-019-1799-6
|
| will show stronger performance. But Kheiron appears to be
| ahead as far as proving the value of the tool since they
| have actually validated prospectively.
| gdebel wrote:
| This is exacerbated by the fact that if the AI told the
| doctor that there is a doubt, no doctor will take the risk
| of not doing a biopsy / scanner / MRI / surgery (depending
| on the case). Because, how would you defend yourself in
| front of the judge ? This is something we always have in
| mind.
|
| This is how you end with false positives and over-
| diagnosis.
| ps2fats wrote:
| You can hardly conclude that broadly screening populations
| are ineffective from this study. You have to consider,
| among other things, the treatments available for the given
| disease being screened and the cost of that screening
| program. If treatments for the disease already have a low
| success rate (what is low?), the timing of detection
| doesn't really help. Additionally, if the cost of the
| screening program is negligible (what is negligible?), then
| even successfully treating a few patients may be worth it.
| enriquto wrote:
| > First, it took me a very long time to really, fully get that
| AI is not fundamentally different from a simple linear
| regression (infering a rule from data).
|
| I'm quite surprised by this. Doesn't each AI tutorial start by
| stating that very thing?
| gdebel wrote:
| I assumed that basic ML was similar to the statistics I
| already knew, and that deep learning was inherently
| different. It had to be different, given how people talked
| about it. It is just an illustration of how the fuss made
| about AI at this time impacted researcher's minds with no
| expertise in ML. This is going down, fortunately.
| dbs wrote:
| I worked for a couple of years providing modelling services to
| health institutions. We did the heavy work under the hood,
| while med practicioners were mostly interested in getting
| academic papers published.
|
| For companies with the enterprise expertise it is difficult to
| enter with the right mindset because sales cycle ib healthcare
| is way too long.
|
| Confirm that that major botlenecks are indeed getting data in
| shape for modelling. Feature engineering is key and domain
| specific. Forget brute force approaches like DL.
|
| Also most AI practicioners in the field seem to ignore that
| doctors dont need to know who is sick but who is actionable.
| Its a completely different game.
| krcz wrote:
| While I don't think AI should replace humans in describing
| medical images, it can be used to check if they might have
| missed something. Such AI-based description should be provided
| only after the human finishes analyzing the image, to avoid
| lazy technicians just copying algorithmic output. The goal
| doesn't have to be increasing accuracy and not doing biopsies,
| it might be reducing number of false negatives.
| rscho wrote:
| Then technicians will just put whatever diagnosis in the
| relevant text field and let the "AI" do their job (if the
| "AI" is deemed good enough). I've been working in healthcare
| for 15 years, and I don't have a single doubt that that's
| what would happen. Conversely, if the "AI" is deemed not good
| enough, it will be business as usual and nobody will so much
| as glance at the "AI" results.
| krcz wrote:
| My idea was:
|
| 1. Technician writes down their diagnosis
|
| 2. They submit it to the system
|
| 3. AI comes with its own analysis
|
| 4. Technician sees the outcome, they can update their
| assessment
|
| 5. Everything is saved into the system
|
| If one of technicians has too much errors in their initial
| assessments, it should raise a concern.
| rscho wrote:
| > 4. Technician sees the outcome, they can update their
| assessment
|
| Will result in exactly what I described above.
|
| > If one of technicians has too much errors in their
| initial assessments, it should raise a concern.
|
| People will refuse AI oversight if there are associated
| sanctions. People will make every effort to game the
| system. Following that, you'll be left with:
|
| a. Pay techs more, so they accept the new working
| conditions.
|
| b. Fire all techs and make do with a (potentially
| suboptimal) AI system.
|
| Yes, this is very much gate keeping at work.
| dontreact wrote:
| I think you are not being creative enough about how AI can
| influence medical care, and also not aware of existing deployed
| solutions making significant clinical impact.
|
| For example, viz.ai has a solution to help get brain bleeds
| spotted to the eyes of surgeons more quickly. It is deployed
| and has cut the average length of stay in the neuro ICU
| significantly
|
| https://mobile.twitter.com/viz_ai/status/1314710308603133953
|
| I work at Caption Health, where we are enabling novices to take
| echocardiography scans. The doctors who work with our
| technology found it extremely useful to help diagnose cardiac
| involvement during covid.
|
| https://captionhealth.com/education/
|
| As much as I have respect for the expertise of medical doctors,
| I would ask that you have respect for folks working to apply AI
| in medicine.
| gdebel wrote:
| Hi, I did not intend to be disrespectful, sorry if you read
| my message like this.
|
| I mainly intended to underline the fact that we (doctors)
| were promised a revolution in healthcare (AKA : to disappear)
| and we ended with diagnostic scores.
|
| However, I gladly admit that I exaggerated and that AI
| technologies can be helpful in some cases, of course.
| dontreact wrote:
| Geoffrey Hinton really made things hard for folks on the AI
| side and even walked back that promise.
|
| I think it's the classic thing where it's overestimated in
| the short term and underestimated in the long (longggg)
| term.
|
| My sense is that for AI to have the full impact it will one
| day reach, it will take rethinking medical care entirely
| with online machine learning and data at the core of how
| decisions are made.
|
| ML was able to revolutionize how ads are delivered (for
| better or worse, but at least reaching the objectives of
| those who deployed it) because you can update and deploy
| the models multiple times a day.
|
| If we can one day get to a world like that where an ML mode
| is constantly learning and updating itself, and has seen
| far more patients than any individual doctor, then maybe we
| will see the sorts of bigger shifts that were imagined
| shortly after we started to see ML surpass human ability on
| long standing difficult tasks like object recognition.
|
| Getting there is a long, long road where we need to learn
| to work together with AI and figure out where the holes are
| in terms of robustness, earn trust over years of successful
| deployment, and figure out how to properly put safety
| bounds around more frequent updates to models.
| gdebel wrote:
| I agree on the very long term possibilities. However, the
| first problem to solve is the data collection. Saving
| doctors and nurses from their horrible professional
| softwares and replacing them with user-friendly, well-
| thought, data collection friendly softwares would be a
| huge step forward.
| dontreact wrote:
| There are certainly tons of people working on this. I
| think that the entrenched competitors will only be
| displaced by other folks who are achieving things they
| cannot via AI. These two problems are closely linked for
| sure.
| jjcon wrote:
| Non clickbait headline: Medicine has some data collection biases
| (if your aim is to represent US demographics).
|
| Long existing non-ML methods suffer due to this data collection
| bias but again for some reason the author seems to put AI in a
| special mysterious place on a pedestal. They use innuendo and
| anecdote to make assertions without evidence to back them up as
| systemic problems unique to AI. Innovation will never happen if
| new technology has to perform perfectly, it only has to perform
| better than existing methods.
| BadInformatics wrote:
| I'm of two minds about this article. It does a reasonable job
| of enumerating the issues with naively deploying ML in a
| healthcare setting. However, these articles are becoming a dime
| a dozen and there is little actionable talk on _how_ to
| discover or mitigate these issues at a level that practitioners
| can use.
|
| To your point about the bar for new tech, I agree that singling
| out AI/ML is a cheap shot and more speculative FUD without
| concrete evidence. That said, we have seen no shortage of
| hucksters and self-aggrandizing members of the "move fast and
| break things crowd" trying to treat medicine as a beginner-
| level Kaggle challenge. This has become particularly egregious
| and noticeable during the pandemic [1]. The respective lack of
| medical and technical literacy among programmers/"data
| scientists" and healthcare providers/admins is just more fuel
| for the fire.
|
| [1]
| https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_w...
| rscho wrote:
| The current effort towards medical ai is heading in the wrong
| direction: were trying to make machines adapt to the field while
| we should be trying to adapt the field to make it available to
| machine-aided reasoning. Problem: almost nobody understands both
| medicine and machines well enough to bridge the abysmal
| communication gap separating AI/CS and medical professionals.
| vagrantJin wrote:
| I think your reason is dubious at best and thoroughly
| impractical. Machines aid our work. We dont work to aid
| machines. The problems in medicine are hard because biology is
| hard. I dont think we understand the depth of knowledge we have
| yet to uncover. Not really. We intuit it but we don't know.
| rscho wrote:
| The medical field is currently supported by clinical
| intuition much more than by hard data, or anything included
| into the common definition of "science". We should absolutely
| work to make medical information systems available to
| machines. Actually, "AI" won't work well until this happens.
| dontreact wrote:
| It's a hard problem to work around which is rooted in the data
| available. I published this paper while I was at Google:
| https://www.nature.com/articles/s41591-019-0447-x
|
| The only data we were able to get at the time was mostly white
| patients. We talked to many hospitals but many were/are reluctant
| to share anonymized data for research. I'm not at Google so I'm
| not sure the status of the project now, but there was a real
| attempt to try and gather more diverse data. Unfortunately there
| were a lot of obstacles put up by those who have the data
| (hospital systems).
|
| Fundamentally, it seems to me like there just aren't as many lung
| cancer screening scans out there for non-white patients as there
| are for white patients. How do we get around this? How do we
| improve on the situation? I fundamentally believe that machine
| learning in the long term can make medicine more accessible to
| more diverse groups, but not if we shoot it down out of
| fearmongering right away.
|
| I agree that bias is a problem, but part of what needs to happen
| to get more diverse data is simply having more data available.
| There is real promise in this technology and if we have a one
| dimensional view of it ("is it or is it not dangerous because of
| bias/privacy") then we will fail to get past the initial humps
| related to fear and distrust.
| epmaybe wrote:
| I certainly see and empathize where you are coming from.
|
| However, I would like to add that it kind of _makes sense_ that
| you'd have more white people with scans available. Focusing on
| the USA for a second (and note that this likely applies
| elsewhere too, since screening programs are really only in full
| force in developed countries which, surprise surprise, are
| predominantly white). Non white patients don't get screened as
| much. Non white patients don't go to the doctor as much. Non
| white patients are inherently fewer than white patients.
|
| I agree that finding a good way to get anonymized data is going
| to help in future endeavors, but we do need to keep in context
| the players involved in getting and using that data.
|
| And of course the ultimate goal, to improve health regardless
| of race, social class, wealth, etc.
| jsinai wrote:
| > Non white patients are inherently fewer than white patients
|
| Look at global population statistics. While there are no
| official global figures for ethnicity, we can make some
| simple inferences based on continental distribution [1]:
|
| North America + Europe combined (17.19%) is barely as much as
| Africa (17.2%), and this is ignoring the fact that a good
| part of the North American population is non-white. There is
| nothing "inherent" about there being less non-white patients.
| The issue is inbalanced access to health care and screening
| programmes, but that is not inherent.
|
| This is without even mentioning that Asia accounts for almost
| 60% of the global population.
|
| [1] https://en.wikipedia.org/wiki/Demographics_of_the_world#2
| 020...
| epmaybe wrote:
| I think I am agreeing with you, based on your comment on
| imbalanced access to healthcare and screening programs. I'm
| saying the same thing in that data collection for ct scans
| is really only happening in countries that are
| predominantly white, not that it isn't possible for other
| countries to implement programs and collect that data for
| training purposes.
|
| Edit: unless of course you have found large databases that
| suggest my intuition is wrong?
| jsinai wrote:
| > Fundamentally, it seems to me like there just aren't as many
| lung cancer screening scans out there for non-white patients as
| there are for white patients.
|
| Just to qualify, you mean for the USA alone? It seems to me
| that part of the challenge is recognizing that the research
| needs to take place beyond just Western countries, or
| acknowledging it where such research is already occurring.
| Understandably many people would not be so comfortable with
| Google accessing patient data from around the world, so the
| next challenge is how diverse and global data can be protected
| so that important medical research can take place without any
| compromise of privacy.
|
| The challenge is hard but surely not impossible, as this was
| the approach taken by the AstraZeneca-Oxford (and others) which
| conducted part of its covid vaccine trials in South Africa to
| test efficacy on non-white populations.
| dontreact wrote:
| At the time we were conducting this research lung cancer
| screening existed mostly in Europe, China and the U.S.
|
| Note that if we had to conduct a 5 year multi site lung
| cancer screening trial ourselves in addition to doing the
| research, there would be basically no way of getting private
| funding for that. Those trials are very, very expensive and
| take several years to reach a conclusion.
|
| Add to that the potential optics of Google "experimenting" in
| developing countries and the blowback risk from that...
| aisofteng wrote:
| As a fellow practitioner, I entirely agree. Actually, reading
| this article made something click for me regarding the oft
| discussed and denigrated "bias in AI" always brought up in
| discussions of the "ethics of AI": there is no bias problem in
| the algorithms of AI.
|
| AI algorithms _need_ bias to work. This is the bias-variance
| trade off: https://en.m.wikipedia.org/wiki/Bias-
| variance_tradeoff
|
| The problem is having the _correct_ bias. If there are
| physiological differences in a disease between men and women
| and you have a good dataset, the bias in that dataset is the
| bias of "people with this disease". If there is no such well-
| balanced dataset, what is being revealed is a pre-existing
| harmful bias in the medicinal field of sample bias in studies.
|
| If anything, we should be thankful that the algorithms used in
| AI, based on statistical theory that has carefully been
| developed over decades to be objective, is revealing these
| problems in the datasets we have been using to frame our
| understanding of real issues.
|
| Next up, the hard part: eliminating our dataset biases and
| letting statistical learning theory and friends do what they
| have been designed to do and can do well.
| jjcon wrote:
| > AI algorithms _need_ bias to work. This is the bias-
| variance trade off: https://en.m.wikipedia.org/wiki/Bias-
| variance_tradeoff
|
| To be clear, statistical bias is in fact distinct from the
| colloquial term 'bias' most people use - but they can be
| interpreted similarly if given the proper context (which you
| did)
| YeGoblynQueenne wrote:
| In machine learning the "bias" that relates to the bias-
| variance tradeoff is _inductive_ bias, i.e. the bias that a
| learning system has in selecting one generalisation over
| another. A good quick introduction to that concept is in
| the following article:
|
| _Why We Need Bias in Machine Learning Algorithms_
|
| https://towardsdatascience.com/why-we-need-bias-in-
| machine-l...
|
| The article is a simplified discussion of an early
| influential paper on the need for bias in machine learning
| by Tom Mitchell:
|
| _The need for bias in learning generalizations_
|
| http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/Need%20f
| o...
|
| The "dataset bias" that you and the other poster are
| discussing is better described in terms of sampling error:
| when sampling data for a training dataset, we are sampling
| from an unknown real distribution and our sampling
| distribution has some error with respect to the real one.
| This error manifests as generalisation error (with respect
| to real-world data, rather than a held-out test set),
| because the learning system learns the distribution of its
| training sample. Unfortunately this kind of error is
| difficult to measure and is masked by the powerful
| modelling abilities of systems like deep neural networks,
| who are very capable at modelling their training
| distribution (and whose accuracy is typically measured on a
| held-out test set, sampled with the same error as the rest
| of the training sample). It is this kind of statistical
| error that is the subject of articles discussing "bias in
| machine learning".
|
| Inductive bias has nothing to do with such "dataset bias
| and is in fact independent from dataset bias. Rather,
| inductive bias is a property of the learning system (e.g. a
| neural net architecture). Consequently, it is not possible
| to "eliminate" inductive bias - machine learning is
| impossible without it! The two should absolutely not be
| confused, they are not similar in any context and should
| not be interpreted as in any way similar.
| usrnm wrote:
| Honest question: does it really matter for lung cancer? Is
| there much difference between races in this particular field?
| dontreact wrote:
| I spoke with one of the doctors who designed the criteria for
| determining whether a lung module found not through screening
| is cancer. He mentioned that they very nearly added a
| different criteria for Asian women, but were too worried
| about the potential backlash.
| dnautics wrote:
| How would you know without the data? There are plenty of
| medical conditions with wildly divergent rates and
| pathophysiologies based on human genetics.
| DoreenMichele wrote:
| _How do we improve on the situation?_
|
| Given economic realities and racist history (consider what
| happened in Tuskegee as one example), in the US you would need
| to provide free screenings to poor people under circumstances
| that convinced people of color they can trust you while signing
| the documents to let you have their data.
|
| This is a fairly high bar to meet and one most studies are
| probably making zero effort to really meet.
|
| I'm part Cherokee and I follow a lot of Natives on Twitter due
| to sincere and legitimate interest in my Native heritage, but
| the world deems me to be a White woman so I am sometimes met
| with hostility simply for trying to talk with Native people
| while looking too White to be trustworthy. Prior positive
| engagement with specific individuals seems to carry little
| weight and be rapidly forgotten. The slightest misstep and,
| welp, "she's an evil White bitch, here to fuck over the Natives
| -- like they always are!"
|
| I'm not blaming people of color for feeling that way. I'm just
| saying that's the reality you are up against.
|
| As someone who spent some years homeless and got a fair amount
| of "help" offered of the "God, you clearly are an idiot causing
| your own problems and need a swift kick in the teeth as part of
| my so-called help" variety, I really sympathize with such
| reactions.
|
| White people often have little to no understanding of the lives
| of people of color and little to no desire to try to really
| understand because really understanding it involves
| understanding systemic racism in a way that tends to make
| Whites very uncomfortable. It veers uncomfortably close to
| self-accusation to honestly try to see how the world is
| experienced by such people.
| dontreact wrote:
| Note that lung cancer screening is covered my Medicare and
| thus already free for anyone over 65 who smoked a pack a day
| for 30 years (or equivalent aka more in less time).
|
| My understanding is that there are many reasons that
| screening is not deployed more widely but the fact that it
| requires a 40 minute discussion with a physician, and those
| physicians in communities in need have very limited time.
|
| Then there is the issue of getting people to show up and take
| part in preventative care which is itself tricky.
|
| In any case, it was not something we were in a position to do
| much about as a small AI research team. Where I work now
| there is also a focus on trying to address this issue by
| reaching out to more hospitals to gather more diverse data,
| but there are still a lot of roadblocks to sharing data we
| have to work through and it's a very slow process.
| DoreenMichele wrote:
| I vaguely recall some article about bathtubs being given to
| poor people in Appalachia who had no running water (in like
| The Great Depression of the 1930s). They would put them on
| the front porch and use them to store coal, which got
| mocked by others as them being "ignorant fools" who didn't
| understand what a bathtub was for rather than seen as
| evidence that bathtubs are essentially useless for bathing
| if you lack running water.
|
| If we nominally have _free_ care available to everyone but
| there are systemic road blocks that make it essentially
| impossible for most people of color to access, this is one
| of those things that falls under "White-splaining."
|
| "Oh, you just do x and it's free" only x is nigh impossible
| to accomplish if you aren't a fairly well off White person
| is one of those things that falls under "systemic racism
| that Whites don't really want to understand."
|
| There's a classist forum that charges $5 for membership and
| claims this is merely to prevent bots from signing up and
| is not intended to keep out poor people and all you have to
| do is ask and they will give you a membership for free if
| it's a financial hardship. And then the mods make sure to
| be openly assholish to poor people so poor people won't
| ask.
|
| When I went to gift a free membership for a "sock puppet"
| account to an existing member who had said in MeTa she
| couldn't afford one but needed one for privacy reasons, the
| mods were quite assholish to me about the entire thing
| every step of the way in every way possible, including
| telling me _after_ I had paid "She could have a free
| second account now for that purpose just for asking" --
| something they also hadn't volunteered to her when she said
| in MeTa she wanted one and couldn't afford it.
|
| It's important that it was in MeTa because that's the only
| part of the site the mods are required to read all of, so
| you can't say they just didn't see it. They saw it and
| declined to inform her "Oh, that's also free for the asking
| if it's a financial hardships for you. That policy is not
| only for initial accounts. If you need a sock puppet for
| privacy and safety reasons, just message us." And then
| offered to refund me my $5 that I had paid to gift her the
| account while I was still homeless.
|
| They also did not offer to hook me up with a second account
| for free. I had eventually paid for a second account for
| myself while homeless and they didn't offer to refund me $5
| at that time either.
|
| I had used the ability to leave anonymous comments a few
| times and they messaged me to let me know I was a bad girl
| and a fuck up who was misusing the system as most people
| only ever left one or two anonymous comments in the entire
| lifetime of their membership. Nowhere was there any
| instructions that you should only do that once or twice. It
| was just the social norm that most people who participated
| a lot and had privacy concerns had a second sock puppet
| account for that purpose.
|
| Rather than going "Oh, she's extremely poor and can't
| afford a second account because she's homeless" they
| treated me like I was misbehaving. I had no idea I was
| doing anything "different" until then in part because I was
| shunned socially because of the extremely toxic classist
| environment that was openly hateful to me where the mods
| actively encouraged other members to bully me.
|
| People of color are painfully well aware that the rules are
| often de facto different for them. People of color often
| are not notified that X can be had for free or are
| oblivious to the ways in which it's not really free if you
| don't already have access to a great deal of infrastructure
| that Whites have access to on a routine basis and people of
| color often simply do not have that infrastructure already
| in place, much like people in Appalachia who can't take a
| bath even if you give them a free tub because their shack
| has no running water.
|
| Saying "It's already free...if you can check this box that
| requires a personal jet to check off" means it's not
| actually free to most people. It's only free to the current
| Haves.
|
| Such policies mean that a lot of "freebies" in the US
| amount to perks for the mostly white Haves, not basic
| healthcare for all people, regardless of socioeconomic
| status or skin color.
| dontreact wrote:
| Yeah... I was just trying to explain what the challenges
| are in hopes that you have a better understanding of what
| it will take to fix it. For example, the requirement that
| you have a physician explain things I think should be
| relaxed as much as is feasible. I'm not blaming people
| who are poor for not having access to healthcare. Also...
| I'm not white.
| DoreenMichele wrote:
| I'm just talking. That's it.
|
| Have a good day.
| nitwit005 wrote:
| Feels like a rant about health care bias that was updated to
| include some mention of ML, but is still mostly about issues that
| existed previously.
| worik wrote:
| One problem is mistaking statistical analysis for learning, data
| for knowledge
| visarga wrote:
| It's only a problem if it doesn't work.
|
| I'm happy with imperfect protein folders that beat SOTA by 100%
| and with DALL.E drawing the radish-in-tutu-walking-a-dog and a
| harp-snail on request. I'll be happy also with the slightly
| unexplainable medical diagnosis that still beats the average
| expert in that field. And getting good unbiased data for these
| algorithms is going to happen eventually.
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