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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

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.
    - rdedev 5y ago
      The difference is that a doctor can try to explain his findings and collaborate with others. This includes not just looking at the scan data but also reading other test results and patient history. ML is not capable of this as far as I know.
      - carlmr 5y ago
        >This includes not just looking at the scan data but also reading other test results and patient history. ML is not capable of this as far as I know. ML is capable of doing that if you train it to do that.
    - esyir 5y ago
      Correct, the target should be instead better than Xth percentile humans across the majority of cases, with the failure modes categorized. I don't get this perfection requirement that gets put on so many systems here. No system is perfect, the question is what failure rate you accept.
      - gjulianm 5y ago
        You're looking at this in isolation instead of in the context of a diagnostic process. Thinking of medical ML models in terms of benchmarks does not make much sense, you're interested in whether they actually change the process. So yeah, maybe you get a model with 5% false negatives where humans have 10% false negatives. That's good. However, when you go to apply that model in reality, what happens to those 5% where model and doctor disagrees? First, we should think about the kind of disagreement. In most of those cases I'd bet that the doctors see something wrong but can't say what, not that the model says "this is bad" and the doctor "it's not". Second, we need to think about what happens when doctor and model disagree. As the model is not 100% accurate, the doctor doesn't know if the model is mistaken or if they're the ones mistaken, so they'll probably order more tests anyways. If it can be something serious, it's worth it to do an extra test to make sure. They'd probably ordered those tests anyways if they weren't sure of what was happening, model or not. So what did the model for a single test change? Did it really change the diagnostic outcomes? What's the actual benefit of the model? How much it's worth to get from 10% to 5% false negatives with a model for a single test if just adding more tests (say, with the same 10% false negatives) to the mix can give you a 1%, 0.1% false negative rate? That's my point. Unless accuracy is really high, ML models are not going to remove uncertainty in diagnostics, few diagnostics consist of just a single test. Benchmarking models against human performance in a single test is not a metric that can drive implementation in the real world.
rdedev 5y ago
The place where I feel ML can help is finding out new disease markers or help upscale the quality of a scan (something I believe Facebook is looking at)
exporectomy 5y ago
If we can have cheap scanners or other instruments to go with the AI, I think it could easily appear as a screening tool that less qualified medical professionals (midwives, nurses, GPs) or even the patients themselves can use. Those people already do various rough screening tests and refer the patient up the chain of expertise according to the outcome.
- gjulianm 5y ago
  > If we can have cheap scanners or other instruments to go with the AI That's a big if already. Not to mention that a lot of times you don't want to give unnecessary radiation to patients, so they wouldn't be interested in making, say, X-rays more accesible. > screening tool that less qualified medical professionals (midwives, nurses, GPs) or even the patients themselves can use But how beneficial would that be? When you need an scan, it's probable that something weird is happening, and you'll probably need more tests than just a scan, and also some specialist that knows which tests to order and what conditions to consider. Once you think of it in that way, it makes less sense. You'd be investing money, resources, training, time into a solution that might benefit only a small subset of patients (those with something weird but that can easily be discarded by a cheap scanner with high certainty without needing an specialist), without even being sure of what actual clinical benefits you would produce. It's far better to invest and research in other areas.
  - exporectomy 5y ago
    I was thinking more like ultrasound. If some crude scanner was cheap enough for every GP's office to have, and an AI could decipher the noisy signal which is perhaps too messy for a human to read, it could be used routinely like blood pressure measurement and stethoscopes are. If it's a regular screening test rather than a response to the patient's complaint, then false negatives wouldn't be as much of a problem because at least it's better than nothing.
    - gjulianm 5y ago
      Ultrasound scanners are fairly cheap. But operating them is not easy, you still need training to use them and know what you're looking at. > it's better than nothing. Not necessarily, that's the issue with screening asymptomatic people. You have to balance the consequences and rates of false positives with the benefits of true positives. If ultrasound screening mostly catches indolent diseases, or those where catching them so early doesn't affect outcomes too much; and you have a lot of people going through unnecessary/risky tests, you might end up doing more damage with those screenings. It's not so easy.
rscho 5y ago
> modern machines can detect anomalies but doctors still learn how to interpret EKGs and double check what the machine says. Uh, TBH no professional so much as glances through the automated EKG summary. It's utterly useless and could be deleted with zero consequences.
- Enginerrrd 5y ago
  I've seen a lot of ECG results, and I always did my own interpretation, but I always found the machine to be accurate, which is unsurprising since distinguishing rhythms is very codified anyway. My only issue with the machine is that it's not really doing something difficult that needs doing. You still need someone that understands how to read ECG's and make decisions about patient management based on past and present conditions. That is the hard part. I'm curious though. Can you elaborate on the types of issues you have encountered? What brand of equipment were you using?
- rubatuga 5y ago
  It's actually improved a lot in the past few years with CNNs being used: https://stanfordmlgroup.github.io/projects/ecg/ https://stanfordmlgroup.github.io/projects/ecg/ No chance that a dinky ECG from 10 years ago is running a neural net inside.
delfinom 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. Radiologists say this until they get malpractice lawsuits. Seriously, I once injured my wrist and the first radiologist said it was just a sprain, but no surprise the hospital was using a outsourced...overseas provider for that quick analysis. The hospital had a inhouse radiologist review it as a course of business later on in a few days and the hospital panicked with phone calls to get me back in because there were numerous wrist bone fractures and they took the first step towards liability issues Could an AI do it? Sure. But you sure as hell need a second opinion
- ixacto 5y ago
  Sure but why not take the best of both worlds? Make a ai analyze your wrist fracture and also a radiologist analyze it. If the results don’t agree then you need to re-analyze. That would even drive down the possibility of malpractice.
  - throwaway4400 5y ago
    A radiologist would have insurance in place for malpractice that indemnifies her/him. So your suggestion that a radiologist would be motivated to use AI to reduce this risk does not seem applicable to me. The idea to run AI alongside the human tool seems to be the current default suggestion these days, now that we have collectively realised that AI cannot do anything serious on its own. Pair it up with a human and the two can work together, with some handholding. But it is a hard sell, the software is now just duplicating a capability, and all of the issues that raises of potentially having two answers to one question. As a radiologist business, you would have to dedicate manpower to investigating when the AI has a different answer. Honestly, if I had a radiology practice, considering the points above, my answer would be no.
sjg007 5y ago
Your argument would be better if you stated that most diseases are diagnosed at an advanced stage when it's obvious. The rest are incidental and then we sometimes occasionally we get lucky. We don't routinely CT scan people because it's harmful to do so.

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.
  - rdedev 5y ago
    The difference is that a doctor can try to explain his findings and collaborate with others. This includes not just looking at the scan data but also reading other test results and patient history. ML is not capable of this as far as I know.
    - carlmr 5y ago
      >This includes not just looking at the scan data but also reading other test results and patient history. ML is not capable of this as far as I know. ML is capable of doing that if you train it to do that.
  - esyir 5y ago
    Correct, the target should be instead better than Xth percentile humans across the majority of cases, with the failure modes categorized. I don't get this perfection requirement that gets put on so many systems here. No system is perfect, the question is what failure rate you accept.
    - gjulianm 5y ago
      You're looking at this in isolation instead of in the context of a diagnostic process. Thinking of medical ML models in terms of benchmarks does not make much sense, you're interested in whether they actually change the process. So yeah, maybe you get a model with 5% false negatives where humans have 10% false negatives. That's good. However, when you go to apply that model in reality, what happens to those 5% where model and doctor disagrees? First, we should think about the kind of disagreement. In most of those cases I'd bet that the doctors see something wrong but can't say what, not that the model says "this is bad" and the doctor "it's not". Second, we need to think about what happens when doctor and model disagree. As the model is not 100% accurate, the doctor doesn't know if the model is mistaken or if they're the ones mistaken, so they'll probably order more tests anyways. If it can be something serious, it's worth it to do an extra test to make sure. They'd probably ordered those tests anyways if they weren't sure of what was happening, model or not. So what did the model for a single test change? Did it really change the diagnostic outcomes? What's the actual benefit of the model? How much it's worth to get from 10% to 5% false negatives with a model for a single test if just adding more tests (say, with the same 10% false negatives) to the mix can give you a 1%, 0.1% false negative rate? That's my point. Unless accuracy is really high, ML models are not going to remove uncertainty in diagnostics, few diagnostics consist of just a single test. Benchmarking models against human performance in a single test is not a metric that can drive implementation in the real world.

rdedev 5y ago

The place where I feel ML can help is finding out new disease markers or help upscale the quality of a scan (something I believe Facebook is looking at)

exporectomy 5y ago

If we can have cheap scanners or other instruments to go with the AI, I think it could easily appear as a screening tool that less qualified medical professionals (midwives, nurses, GPs) or even the patients themselves can use. Those people already do various rough screening tests and refer the patient up the chain of expertise according to the outcome.

gjulianm 5y ago
> If we can have cheap scanners or other instruments to go with the AI That's a big if already. Not to mention that a lot of times you don't want to give unnecessary radiation to patients, so they wouldn't be interested in making, say, X-rays more accesible. > screening tool that less qualified medical professionals (midwives, nurses, GPs) or even the patients themselves can use But how beneficial would that be? When you need an scan, it's probable that something weird is happening, and you'll probably need more tests than just a scan, and also some specialist that knows which tests to order and what conditions to consider. Once you think of it in that way, it makes less sense. You'd be investing money, resources, training, time into a solution that might benefit only a small subset of patients (those with something weird but that can easily be discarded by a cheap scanner with high certainty without needing an specialist), without even being sure of what actual clinical benefits you would produce. It's far better to invest and research in other areas.
- exporectomy 5y ago
  I was thinking more like ultrasound. If some crude scanner was cheap enough for every GP's office to have, and an AI could decipher the noisy signal which is perhaps too messy for a human to read, it could be used routinely like blood pressure measurement and stethoscopes are. If it's a regular screening test rather than a response to the patient's complaint, then false negatives wouldn't be as much of a problem because at least it's better than nothing.
  - gjulianm 5y ago
    Ultrasound scanners are fairly cheap. But operating them is not easy, you still need training to use them and know what you're looking at. > it's better than nothing. Not necessarily, that's the issue with screening asymptomatic people. You have to balance the consequences and rates of false positives with the benefits of true positives. If ultrasound screening mostly catches indolent diseases, or those where catching them so early doesn't affect outcomes too much; and you have a lot of people going through unnecessary/risky tests, you might end up doing more damage with those screenings. It's not so easy.

rscho 5y ago

> modern machines can detect anomalies but doctors still learn how to interpret EKGs and double check what the machine says. Uh, TBH no professional so much as glances through the automated EKG summary. It's utterly useless and could be deleted with zero consequences.

Enginerrrd 5y ago
I've seen a lot of ECG results, and I always did my own interpretation, but I always found the machine to be accurate, which is unsurprising since distinguishing rhythms is very codified anyway. My only issue with the machine is that it's not really doing something difficult that needs doing. You still need someone that understands how to read ECG's and make decisions about patient management based on past and present conditions. That is the hard part. I'm curious though. Can you elaborate on the types of issues you have encountered? What brand of equipment were you using?
rubatuga 5y ago
It's actually improved a lot in the past few years with CNNs being used: https://stanfordmlgroup.github.io/projects/ecg/ https://stanfordmlgroup.github.io/projects/ecg/ No chance that a dinky ECG from 10 years ago is running a neural net inside.

delfinom 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. Radiologists say this until they get malpractice lawsuits. Seriously, I once injured my wrist and the first radiologist said it was just a sprain, but no surprise the hospital was using a outsourced...overseas provider for that quick analysis. The hospital had a inhouse radiologist review it as a course of business later on in a few days and the hospital panicked with phone calls to get me back in because there were numerous wrist bone fractures and they took the first step towards liability issues Could an AI do it? Sure. But you sure as hell need a second opinion

ixacto 5y ago
Sure but why not take the best of both worlds? Make a ai analyze your wrist fracture and also a radiologist analyze it. If the results don’t agree then you need to re-analyze. That would even drive down the possibility of malpractice.
- throwaway4400 5y ago
  A radiologist would have insurance in place for malpractice that indemnifies her/him. So your suggestion that a radiologist would be motivated to use AI to reduce this risk does not seem applicable to me. The idea to run AI alongside the human tool seems to be the current default suggestion these days, now that we have collectively realised that AI cannot do anything serious on its own. Pair it up with a human and the two can work together, with some handholding. But it is a hard sell, the software is now just duplicating a capability, and all of the issues that raises of potentially having two answers to one question. As a radiologist business, you would have to dedicate manpower to investigating when the AI has a different answer. Honestly, if I had a radiology practice, considering the points above, my answer would be no.

sjg007 5y ago

Your argument would be better if you stated that most diseases are diagnosed at an advanced stage when it's obvious. The rest are incidental and then we sometimes occasionally we get lucky. We don't routinely CT scan people because it's harmful to do so.