10 ms·

Machine learning is booming in medicine, but also facing a credibility crisis

debarshri 5y ago
This is true for every other domain. When every company calls itself a AI company these days. Therefore value of AI/ML and credibility gets diluted. It becomes very difficult to classify real orgs using hardcode stats from fake ones. When I hear "we are solving x with AI" or "AI driven", it gives me jitters.
- f6v 5y ago
  The elephant in the room is that AI does not exist, even in "hardcore" orgs. A transformer model is not AI.
  - throwaway3699 5y ago
    The classical definition of AI in CS is so broad as it can even include expert systems. I think using that term when the general public sees it differently has done a great disservice to the fields credibility.
  - debarshri 5y ago
    What I realized recently is the AI is subjective thing. AI for an Investor might be simple math for an engineer.
  - martindbp 5y ago
    > A transformer model is not AI. Well, it's _something_ different. It's not "code" or "programming", so whatever you want to call it, that's what's driving the investment hype.
  - qayxc 5y ago
    While I share the sentiment that the term "AI" is applied way too broadly these days, there is a point to be made that it's somewhat difficult to draw a clear line anyway. Back in the early 20th century, chess was considered to be something only an intelligent agent can do. A few decades later programs became competent enough that it took a grand master to beat them. Today, it's hopeless to try and beat a chess program as a human. The next barrier was NLP and understanding/generating text and natural speech. Speech recognition worked reasonably well 20 years ago and is close to human level today. Speech generation - given enough processing power - is now perfectly natural. Text understanding and generation is very close as well. The development of AI bears an interesting resemblance to evolution in that sceptics are busy pointing to "missing links" and as soon as that link is discovered, they chase to the next one. Intelligence itself is defined poorly enough as it is, and watering the term down by slapping the "AI"-label on everything doesn't help that. On the other hand, intelligence is a spectrum and on some aspects of that spectrum, machines have already surpassed humans decades ago (think of calculators, chess, memory, searching and indexing, etc.). IMO, the most important distinction to be made is the difference between AGI and AI. A transformer model is AI in every useful definition of the term "intelligence", but it sure isn't "general intelligence" if only for the fact that it cannot actively query its environment for additional information and has no continuous stream of "consciousness". Before we can dismiss a system as not being AI, we need a sharp enough definition of what we would define as AI first. A "I know it when I see it"-type of definition isn't helpful and always keeps the door open for the ultimate rejection: "but it still hasn't got a SOUL!"
    - lordgrenville 5y ago
      "As soon as it works, no one calls it AI anymore" - John McCarthy
- beforeolives 5y ago
  I have geniunely considered switching away from data science altogether for this reason.
  - axelroze 5y ago
    Why? The only other branch of IT that pays as good as Data Science/Engineering is Blockchain Developer. The field of AI applications will only grow and thus career opportunities as well. Are people in serious finance bothered that there are a bazzilion of scams every day, including aforementioned blockchains? No. Why should competent AI practitioners care as well?
    - lou1306 5y ago
      > Why should competent AI practitioners care as well? AI took decades to recover from its first boom-bust cycle. No matter how competent you were back then, getting hired or funded to run your AI business/academic project/whatever was hard because the first wave of AI failed to deliver. Sure, now AI/ML/Data Science is very commercially/acadamically viable, but the hype has also grown spectacularly. If the general public fails to manage expectations, another AI winter cannot be ruled out.
- delfinom 5y ago
  Most of the "AI" I see is just linear regressions run on meager datasets which lets them stick on that AI label but could have been written by a person with a little effort.
dkrysl 5y ago
I've been at a few startups and ML has typically meant shoddy rules-based logic or some out of the box model. Only one company applied it with rigor and even then it was a slog - lackluster results, tinkering with different models, poring through research papers to figure out where the cognitive gap came from. The rest of the company thought we were brilliant as did prospective clients. Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work. Businesses are swimming in data but it's siloed or dirty. I don't really see that and the actual analysis being automated away - too much messiness (human error inputting data into systems like Salesforce, ETL breaks in prod leading to gaps, etc).
- debarshri 5y ago
  Shoddy rules-based logic is ok, as long as it created 10x value. When you masquerade it as AI/ML, you are doing disservice to your own company and clients. My question is very simple, at what point Shoddy rules-based engine becomes "AI-driven approach"?
  - antipaul 5y ago
    When you are making the PowerPoint
- qsort 5y ago
  > Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work Which makes me wonder how much of the supposed magical 10x value of AI actually just comes from getting your shit together in terms of ETL and data pipelines.
  - mjburgess 5y ago
    99.95%
gjulianm 5y ago
Some radiologists think that AI will be really good for filtering out normal images, so that they only have to review anomalies. But I don't think that a model that detects diseases will be too successful, even if they manage to make it really work. For one, it's actually difficult to interpret and find signs in radiologic images. Obvious signs are obvious, but there are others that could be image artifacts, or just indolent variations, or point to something serious. Even with a generally good accuracy, it'll be hard that a general model performs well on those anomalies with low prevalence. Second, radiologic signs are just signs. Most diseases are diagnosed with more than just radiologic signs. Most signs are compatible with a lot of diseases. If you see a model that pretends to diagnose a certain disease, well, they're looking at it wrong. Third, you need a way to have responsibility for diagnoses, and a way to find and correct errors. I don't think that's possible with an unsupervised AI, you'll always need a doctor there to check the image and verify the output. There won't be much savings there. Whenever someone says "AI is going to revolutionize medicine", it's really hard to believe them. I mean, you just have to look at EKGs, modern machines can detect anomalies but doctors still learn how to interpret EKGs and double check what the machine says. It's a help, but not a replacement.
- tgv 5y ago
  No disagreement here from my part, but I wouldn't be surprised if imaging artifacts could be detected/fixed by ML. E.g., there has been quite a bit of research in MRI boundary effects, although I wouldn't know if that turned out any practical solutions.
  - gjulianm 5y ago
    The issue with that is that misclassifying something in an image can have serious consequences. So unless ML is 100% accurate, you'll always want an alternative way to check if it's really an artifact or something serious, so, why bother making an accurate model when you'll still double check the results?
    - klodolph 5y ago
      I don't follow. Why would anything need to be 100% accurate? Humans aren't 100% accurate, and there's no reason ML would need to be 100% accurate either.
BiteCode_dev 5y ago
Unless the AI can talk tot the patient to get some context, it's going to be taking decisions with only very partial information, no matter how good it is. Still, I'm thinking that as it improves, it's going to show that doctors are not that good at their job on average, and that's going to be fun to watch.
- bayesian_horse 5y ago
  Medical ML is not about making decisions but about informing decisions. It is a tool for the doctor, not a replacement.
  - BiteCode_dev 5y ago
    That's what it should be, that's not how it's being sold, and how it's going to be used. Just like for wikipedia.
  - eduramiba 5y ago
    I think AI should always be a support tool, but people are always talking about replacing doctors... And a high volume if diverse quality data is fundamental: https://en.capillary.io/posts/ai-medical-diagnosis-guide/ https://en.capillary.io/posts/ai-medical-diagnosis-guide/
    - delfinom 5y ago
      >I think AI should always be a support tool, but people are always talking about replacing doctors... Because Americans are trying to solve their healthcare crisis by attacking the doctors and literally not the entire rest of the industry which controls the spiraling healthcare costs. The end of result if we manage to eliminate doctors is still spiraling out of control healthcare.
- PeterisP 5y ago
  Radiologists aren't talking to the patient either and don't have earlier scans available, they "just" comment on the image; so the doctor who does have the patient, their history and the radiology report(s) might as well receive the report from a ML system instead of a human radiologist.
bayesian_horse 5y ago
I would really like to get a job in medical ML, but without a related degree it seems impossible. I'm "only" a Veterinarian and I don't even get interviews...
- ecmascript 5y ago
  Why not build something on your spare time and try to create your own job then?
  - throwaways885 5y ago
    How would one person bootstrap a venture in medical ML without initial funding or datasets?
    - itsoktocry 5y ago
      Do you think it's the model and the data that's novel? That's exactly why AI is failing. How about starting with a problem to solve? You don't need anything but a pen and paper. Gotta start somewhere.
      - bayesian_horse 5y ago
        A pen and a paper doesn't make you any money. I have tons of ideas. Doesn't mean jack though. In medical AI, you really need datasets. Of the most expensive kind, and lots of it.
      - throwaways885 5y ago
        Getting trusted access to medical datasets is a huge issue, and so is getting clinicians to use the systems you've produced. Both of these problems are usually fixed with large amounts of funding.
  - bayesian_horse 5y ago
    Founding a startup is not an option available to everyone. It takes money and other resources. And for some of the same reasons that I am unemployed despite valuable skills, it is virtually impossible for me to get any funding.
klodolph 5y ago
> By far the biggest problem — and the trickiest to solve — points to machine learning’s Catch-22: There are few large, diverse data sets to train and validate a new tool on, and many of those that do exist are kept confidential for legal or business reasons. This is exactly my sense of the problem. Not saying that this is the only problem, just that this seems like the biggest immediate problem. There are a bunch of questionable ML startups that try to do something with, say, a model trained on ImageNet. You can get pretty far starting with ImageNet. ImageNet was made with Mechanical Turk, but there is no Mechanical Turk for radiologists. If you work really hard you might get patient & treatment notes but interpreting those notes presents its own problem.
- Eridrus 5y ago
  I came away from this convinced that somebody needs to build a data sharing system for medical images. If there was a system for sharing images that had existed before Covid and enough data had been contributed that reviewers could demand evaluation on some predefined test sets, then a lot of these inconsistent evaluation issues could be weeded out. The article mentions federated learning, but I feel like that's solving a different problem than reliable evaluation.
spoonjim 5y ago
> By far the biggest problem — and the trickiest to solve — points to machine learning’s Catch-22: There are few large, diverse data sets to train and validate a new tool on, and many of those that do exist are kept confidential for legal or business reasons. This is why China will win the AI Age.
- axelroze 5y ago
  Very likely. If someone is more interested in the current state of AI on a national level a very good book is AI Superpowers by Kai Fu Lee. Data is the new Oil and USA is still clinging to the old oil while China has AI as number 1 priority.
  - ChuckNorris89 5y ago
    Just because China hoards more data doesn't guarantee any success. If just data collection would be enough then all crime should have been eradicated in the US by how much data NSA has.
    - ThomPete 5y ago
      It guarantees more access to training data which is crucial for development of any superiority when it comes to applied AI. The US still have a better academic culture but the chinese are catching up. Nothing is inevitable.
      - inglor_cz 5y ago
        The strength of the US was always in the attractivity of the country for talented foreigners. Is the same true of China, or will China have to rely on its homegrown cadre of scientists? Granted, there might be a lot of talent in a population of 1,4 billion.
      - j_wtf_all_taken 5y ago
        > The US still have a better academic culture but the chinese are catching up. Science is about truth. Dictatorships are about appealing to the dear leader. That's why China will never catch up to free and open countries.
gumby 5y ago
AI has been breaking its pick at this coalface for at least 50 years, with negligible success. I don’t know why such a difficult field has been so popular. It seems like the only result of consequence has been Zork, developed on the DM machine at the MIT AI lab in the 70s. No, it was not developed as a medical application but I believe that machine was owned by the medical decision making group. Stanford’s Knowledge Systems Lab (Ed Feigenbaum’s lab) was next to and affiliated with the Medical School too.
dilawar 5y ago
NIH should make it mandatory to deposit training data to a repository. Good time to have a protein bank like database for AI models and dataset.
hrzn 5y ago
A lot of these issues also have to do with the publishing system. There is no incentive in publishing stuff that don't work even though they were reasonable things to try. This indirectly pushes a lot of bad/flawed/incomplete papers and results to be published. Even if your methodology is flawed but you try hard enough, you have a >0 probability of getting your paper published somewhere. As part of a solution, I think we would benefit from the existence of prestigious journals for negative results, which would incentivise also publishing what doesn't work. This way researchers wouldn't have to try massaging their data and experiments until it looks like it works. They could just publish that it doesn't work, and it would be good for them too.
dekhn 5y ago
I was a moderately successful scientist who reorganized his career around applying machine learning to medicine. After working in the field for a few years, I noticed that all the folks getting attention for their papers were basically publishing bullcrap. I have a high bar for publishing, I don't publish stuff that is bullcrap. But my competitors were, and they had a serious advantage over me (because most people couldn't recognize the bullcrap). ML folks today are under immense pressure to show small-digit improvements over existing methods and they're using all the wrong techniques (p-hacking, massive hyperparamter runs, press releases that overstate the impact). It's really sad to see all the snake oil.
FL33TW00D 5y ago
No wonder it’s facing a credibility crisis. Almost every single Machine Learning for Neuroimaging paper I see is reporting 0.9+ AUC whilst using <50 subjects. With the rampant lack of statistical rigour it blows my mind they get published. This great paper shows that reported accuracy plummets as dataset size increases: https://hal.inria.fr/hal-01545002/document https://hal.inria.fr/hal-01545002/document
rafaelero 5y ago
So much FUD on this thread. If a doctor can identify something fishy on a x-ray OF COURSE an appropriate machine learning algorithm can do it as well. It's just a question of gathering the right dataset and experimenting with different architectures.

This is true for every other domain. When every company calls itself a AI company these days. Therefore value of AI/ML and credibility gets diluted. It becomes very difficult to classify real orgs using hardcode stats from fake ones. When I hear "we are solving x with AI" or "AI driven", it gives me jitters.

f6v 5y ago
The elephant in the room is that AI does not exist, even in "hardcore" orgs. A transformer model is not AI.
- throwaway3699 5y ago
  The classical definition of AI in CS is so broad as it can even include expert systems. I think using that term when the general public sees it differently has done a great disservice to the fields credibility.
- debarshri 5y ago
  What I realized recently is the AI is subjective thing. AI for an Investor might be simple math for an engineer.
- martindbp 5y ago
  > A transformer model is not AI. Well, it's _something_ different. It's not "code" or "programming", so whatever you want to call it, that's what's driving the investment hype.
- qayxc 5y ago
  While I share the sentiment that the term "AI" is applied way too broadly these days, there is a point to be made that it's somewhat difficult to draw a clear line anyway. Back in the early 20th century, chess was considered to be something only an intelligent agent can do. A few decades later programs became competent enough that it took a grand master to beat them. Today, it's hopeless to try and beat a chess program as a human. The next barrier was NLP and understanding/generating text and natural speech. Speech recognition worked reasonably well 20 years ago and is close to human level today. Speech generation - given enough processing power - is now perfectly natural. Text understanding and generation is very close as well. The development of AI bears an interesting resemblance to evolution in that sceptics are busy pointing to "missing links" and as soon as that link is discovered, they chase to the next one. Intelligence itself is defined poorly enough as it is, and watering the term down by slapping the "AI"-label on everything doesn't help that. On the other hand, intelligence is a spectrum and on some aspects of that spectrum, machines have already surpassed humans decades ago (think of calculators, chess, memory, searching and indexing, etc.). IMO, the most important distinction to be made is the difference between AGI and AI. A transformer model is AI in every useful definition of the term "intelligence", but it sure isn't "general intelligence" if only for the fact that it cannot actively query its environment for additional information and has no continuous stream of "consciousness". Before we can dismiss a system as not being AI, we need a sharp enough definition of what we would define as AI first. A "I know it when I see it"-type of definition isn't helpful and always keeps the door open for the ultimate rejection: "but it still hasn't got a SOUL!"
  - lordgrenville 5y ago
    "As soon as it works, no one calls it AI anymore" - John McCarthy
beforeolives 5y ago
I have geniunely considered switching away from data science altogether for this reason.
- axelroze 5y ago
  Why? The only other branch of IT that pays as good as Data Science/Engineering is Blockchain Developer. The field of AI applications will only grow and thus career opportunities as well. Are people in serious finance bothered that there are a bazzilion of scams every day, including aforementioned blockchains? No. Why should competent AI practitioners care as well?
  - lou1306 5y ago
    > Why should competent AI practitioners care as well? AI took decades to recover from its first boom-bust cycle. No matter how competent you were back then, getting hired or funded to run your AI business/academic project/whatever was hard because the first wave of AI failed to deliver. Sure, now AI/ML/Data Science is very commercially/acadamically viable, but the hype has also grown spectacularly. If the general public fails to manage expectations, another AI winter cannot be ruled out.
delfinom 5y ago
Most of the "AI" I see is just linear regressions run on meager datasets which lets them stick on that AI label but could have been written by a person with a little effort.

dkrysl 5y ago

I've been at a few startups and ML has typically meant shoddy rules-based logic or some out of the box model. Only one company applied it with rigor and even then it was a slog - lackluster results, tinkering with different models, poring through research papers to figure out where the cognitive gap came from. The rest of the company thought we were brilliant as did prospective clients. Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work. Businesses are swimming in data but it's siloed or dirty. I don't really see that and the actual analysis being automated away - too much messiness (human error inputting data into systems like Salesforce, ETL breaks in prod leading to gaps, etc).

debarshri 5y ago
Shoddy rules-based logic is ok, as long as it created 10x value. When you masquerade it as AI/ML, you are doing disservice to your own company and clients. My question is very simple, at what point Shoddy rules-based engine becomes "AI-driven approach"?
- antipaul 5y ago
  When you are making the PowerPoint
qsort 5y ago
> Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work Which makes me wonder how much of the supposed magical 10x value of AI actually just comes from getting your shit together in terms of ETL and data pipelines.
- mjburgess 5y ago
  99.95%

gjulianm 5y ago

Some radiologists think that AI will be really good for filtering out normal images, so that they only have to review anomalies. But I don't think that a model that detects diseases will be too successful, even if they manage to make it really work. For one, it's actually difficult to interpret and find signs in radiologic images. Obvious signs are obvious, but there are others that could be image artifacts, or just indolent variations, or point to something serious. Even with a generally good accuracy, it'll be hard that a general model performs well on those anomalies with low prevalence. Second, radiologic signs are just signs. Most diseases are diagnosed with more than just radiologic signs. Most signs are compatible with a lot of diseases. If you see a model that pretends to diagnose a certain disease, well, they're looking at it wrong. Third, you need a way to have responsibility for diagnoses, and a way to find and correct errors. I don't think that's possible with an unsupervised AI, you'll always need a doctor there to check the image and verify the output. There won't be much savings there. Whenever someone says "AI is going to revolutionize medicine", it's really hard to believe them. I mean, you just have to look at EKGs, modern machines can detect anomalies but doctors still learn how to interpret EKGs and double check what the machine says. It's a help, but not a replacement.

tgv 5y ago
No disagreement here from my part, but I wouldn't be surprised if imaging artifacts could be detected/fixed by ML. E.g., there has been quite a bit of research in MRI boundary effects, although I wouldn't know if that turned out any practical solutions.
- gjulianm 5y ago
  The issue with that is that misclassifying something in an image can have serious consequences. So unless ML is 100% accurate, you'll always want an alternative way to check if it's really an artifact or something serious, so, why bother making an accurate model when you'll still double check the results?
  - klodolph 5y ago
    I don't follow. Why would anything need to be 100% accurate? Humans aren't 100% accurate, and there's no reason ML would need to be 100% accurate either.

BiteCode_dev 5y ago

Unless the AI can talk tot the patient to get some context, it's going to be taking decisions with only very partial information, no matter how good it is. Still, I'm thinking that as it improves, it's going to show that doctors are not that good at their job on average, and that's going to be fun to watch.

bayesian_horse 5y ago
Medical ML is not about making decisions but about informing decisions. It is a tool for the doctor, not a replacement.
- BiteCode_dev 5y ago
  That's what it should be, that's not how it's being sold, and how it's going to be used. Just like for wikipedia.
- eduramiba 5y ago
  I think AI should always be a support tool, but people are always talking about replacing doctors... And a high volume if diverse quality data is fundamental: https://en.capillary.io/posts/ai-medical-diagnosis-guide/ https://en.capillary.io/posts/ai-medical-diagnosis-guide/
  - delfinom 5y ago
    >I think AI should always be a support tool, but people are always talking about replacing doctors... Because Americans are trying to solve their healthcare crisis by attacking the doctors and literally not the entire rest of the industry which controls the spiraling healthcare costs. The end of result if we manage to eliminate doctors is still spiraling out of control healthcare.
PeterisP 5y ago
Radiologists aren't talking to the patient either and don't have earlier scans available, they "just" comment on the image; so the doctor who does have the patient, their history and the radiology report(s) might as well receive the report from a ML system instead of a human radiologist.

bayesian_horse 5y ago

I would really like to get a job in medical ML, but without a related degree it seems impossible. I'm "only" a Veterinarian and I don't even get interviews...

ecmascript 5y ago
Why not build something on your spare time and try to create your own job then?
- throwaways885 5y ago
  How would one person bootstrap a venture in medical ML without initial funding or datasets?
  - itsoktocry 5y ago
    Do you think it's the model and the data that's novel? That's exactly why AI is failing. How about starting with a problem to solve? You don't need anything but a pen and paper. Gotta start somewhere.
    - bayesian_horse 5y ago
      A pen and a paper doesn't make you any money. I have tons of ideas. Doesn't mean jack though. In medical AI, you really need datasets. Of the most expensive kind, and lots of it.
    - throwaways885 5y ago
      Getting trusted access to medical datasets is a huge issue, and so is getting clinicians to use the systems you've produced. Both of these problems are usually fixed with large amounts of funding.
- bayesian_horse 5y ago
  Founding a startup is not an option available to everyone. It takes money and other resources. And for some of the same reasons that I am unemployed despite valuable skills, it is virtually impossible for me to get any funding.

klodolph 5y ago

> By far the biggest problem — and the trickiest to solve — points to machine learning’s Catch-22: There are few large, diverse data sets to train and validate a new tool on, and many of those that do exist are kept confidential for legal or business reasons. This is exactly my sense of the problem. Not saying that this is the only problem, just that this seems like the biggest immediate problem. There are a bunch of questionable ML startups that try to do something with, say, a model trained on ImageNet. You can get pretty far starting with ImageNet. ImageNet was made with Mechanical Turk, but there is no Mechanical Turk for radiologists. If you work really hard you might get patient & treatment notes but interpreting those notes presents its own problem.

Eridrus 5y ago
I came away from this convinced that somebody needs to build a data sharing system for medical images. If there was a system for sharing images that had existed before Covid and enough data had been contributed that reviewers could demand evaluation on some predefined test sets, then a lot of these inconsistent evaluation issues could be weeded out. The article mentions federated learning, but I feel like that's solving a different problem than reliable evaluation.

spoonjim 5y ago

axelroze 5y ago
Very likely. If someone is more interested in the current state of AI on a national level a very good book is AI Superpowers by Kai Fu Lee. Data is the new Oil and USA is still clinging to the old oil while China has AI as number 1 priority.
- ChuckNorris89 5y ago
  Just because China hoards more data doesn't guarantee any success. If just data collection would be enough then all crime should have been eradicated in the US by how much data NSA has.
  - ThomPete 5y ago
    It guarantees more access to training data which is crucial for development of any superiority when it comes to applied AI. The US still have a better academic culture but the chinese are catching up. Nothing is inevitable.
    - inglor_cz 5y ago
      The strength of the US was always in the attractivity of the country for talented foreigners. Is the same true of China, or will China have to rely on its homegrown cadre of scientists? Granted, there might be a lot of talent in a population of 1,4 billion.
    - j_wtf_all_taken 5y ago
      > The US still have a better academic culture but the chinese are catching up. Science is about truth. Dictatorships are about appealing to the dear leader. That's why China will never catch up to free and open countries.

gumby 5y ago

AI has been breaking its pick at this coalface for at least 50 years, with negligible success. I don’t know why such a difficult field has been so popular. It seems like the only result of consequence has been Zork, developed on the DM machine at the MIT AI lab in the 70s. No, it was not developed as a medical application but I believe that machine was owned by the medical decision making group. Stanford’s Knowledge Systems Lab (Ed Feigenbaum’s lab) was next to and affiliated with the Medical School too.

dilawar 5y ago

NIH should make it mandatory to deposit training data to a repository. Good time to have a protein bank like database for AI models and dataset.

hrzn 5y ago

A lot of these issues also have to do with the publishing system. There is no incentive in publishing stuff that don't work even though they were reasonable things to try. This indirectly pushes a lot of bad/flawed/incomplete papers and results to be published. Even if your methodology is flawed but you try hard enough, you have a >0 probability of getting your paper published somewhere. As part of a solution, I think we would benefit from the existence of prestigious journals for negative results, which would incentivise also publishing what doesn't work. This way researchers wouldn't have to try massaging their data and experiments until it looks like it works. They could just publish that it doesn't work, and it would be good for them too.

dekhn 5y ago

I was a moderately successful scientist who reorganized his career around applying machine learning to medicine. After working in the field for a few years, I noticed that all the folks getting attention for their papers were basically publishing bullcrap. I have a high bar for publishing, I don't publish stuff that is bullcrap. But my competitors were, and they had a serious advantage over me (because most people couldn't recognize the bullcrap). ML folks today are under immense pressure to show small-digit improvements over existing methods and they're using all the wrong techniques (p-hacking, massive hyperparamter runs, press releases that overstate the impact). It's really sad to see all the snake oil.

FL33TW00D 5y ago

No wonder it’s facing a credibility crisis. Almost every single Machine Learning for Neuroimaging paper I see is reporting 0.9+ AUC whilst using <50 subjects. With the rampant lack of statistical rigour it blows my mind they get published. This great paper shows that reported accuracy plummets as dataset size increases: https://hal.inria.fr/hal-01545002/document https://hal.inria.fr/hal-01545002/document

rafaelero 5y ago

So much FUD on this thread. If a doctor can identify something fishy on a x-ray OF COURSE an appropriate machine learning algorithm can do it as well. It's just a question of gathering the right dataset and experimenting with different architectures.