7 ms·

This is not exactly new. I remember seeing models that did really well many years ago. And again caught many that humans had miss. The problem is that they f

by btilly 7y ago

This is not exactly new. I remember seeing models that did really well many years ago. And again caught many that humans had miss. The problem is that they fail differently than humans do, in a way that humans wind up not trusting the results. It turns out that there are parts of the breast that are easy to spot tumors in, and parts that are hard. A human scans quickly over the easy areas, and focuses on the hard. The result is that humans make careless errors on the easy areas, and catch hard tumors. Computers make no careless errors, but can't catch the hard ones. Thus when a human sees what the computer caught that the human did not, the mistake is easily dismissed. But when the human sees the ones that the computer missed, it becomes, "It doesn't know how to do the real work." Ideally the two would be used together for better results than either alone. But humans wind up resenting the computer...

hathawsh 7y ago
That's a helpful perspective. Would it be possible for IBM to create a service that allows patients to submit their own pictures for scanning?
- lostlogin 7y ago
  We are at a tricky stage with this. Images are too big to be emailed and many people no longer have CD drives. There is increasing use of tomographic imaging in examinations too, and the files are pretty big.
  - hathawsh 7y ago
    On the other hand, cheap micro SD cards and thumb drives hold far more than CDs or DVDs.
    - lostlogin 7y ago
      We prefer to send the images to the PACS the patient wants them on, as for most imaging that’s all that’s wanted. Obstetric imaging is one massive exception.
  - toomuchtodo 7y ago
    Auth workflow so whomever does the imaging stores the image, and the patient can grant access and a pointer to systems consuming the imaging.
ska 7y ago
I worked on one of those systems. For some screening tasks, it has been at or beating average-radiologist performance since the mid or late 90s. There are a number of issues, but it's true that raw algorithmic performance is a small part of the whole picture.
- RosanaAnaDana 7y ago
  This is often the case in almost all ml-augmented workflows. The augmentation begins and ends with people looking at things, because the machine only knows what you told it, and doesn't care. The real work is building systems around people who do the interpretation and labeling to make their jobs easier.
  - ska 7y ago
    Yes, very much this. And the importance of workflow impact cannot be overstated. The (clinical) system I mention above, the ML part was a few percent of the total effort, max.
- agumonkey 7y ago
  can you tell us more about the rest ?
cma 7y ago
> Thus when a human sees what the computer caught that the human did not, the mistake is easily dismissed. You mean dismissed as in not believing the result or not being impressive? (unclear if that's what you mean later by resenting and if it is tied to this statement)
- btilly 7y ago
  I mean dismissed as in not being impressive. "Yeah, yeah, I should have seen that. It is obvious. No big deal." And if you use the software as a backup, people don't respond well to their dumb mistakes being pointed out. And the result is that people put effort in to not making dumb mistakes...and therefore either slow down or have less time for what humans can do better than computers. In theory it should work a lot better than it actually does.
  - inimino 7y ago
    What if you use the human as the backup? So they are looking for things the ML system missed. (As well as confirming the things it found.)
Symmetry 7y ago
So a lot like the centaur era in chess competitions.
twanvl 7y ago
> The problem is that they fail differently than humans do That is a great argument for giving such a model as an aid to a human doctor. Together they will be better then either one alone.
- onlyrealcuzzo 7y ago
  In Thinking, Fast and Slow -- the author details a double blind trial where the did this. It was worse with humans and AI than with just AI. Humans think they can use AI as a guide and move it in the right direction. But the movements they made, on average, were bad.
  - notahacker 7y ago
    Surely in this type of instance (looking at a scan to answer a yes/no question) the human and AI act independently, with the computer being a useful aid because it separately picks up a few of the human's false negatives. Assuming false negatives are a lot worse than false positives, this can only be a good thing.
    - chongli 7y ago
      If they lead to an unnecessary mastectomy then false positives are pretty bad. Not as bad as dying, obviously, but still a severe blow to a woman's identity and sense of self worth. It's going to be a hard pill to swallow if you have to tell a woman "sorry, we removed your healthy breast because the computer made a mistake."
      - irq11 7y ago
        Even a false positive that leads to telling the patient that they may have cancer is bad. It leads to a life-long anxiety for many people.
      - glangdale 7y ago
        I think the idea of "screening" is that you don't just race off to a mastectomy the minute some AI model goes off. Of course, putting more false positives through a fallible process of review does run the risk you speak of.
benatkin 7y ago
I think this could be a new milestone, compared to what you saw, because it uses deep neural networks. I also am not sure saying computers can't catch the hard ones is true in light of this. It seems like a deep neural network would be useful to catch the hard ones. I agree with using the two together, but I don't see why the two can't be different AI subsystems. That seems to be what they're going for at IBM. Some of the power comes from a scientific model, while more power comes from well-trained deep learning networks. Before Alpha Zero, advances in self-driving cars, and face detection advances, I'd have agreed with you.
neaden 7y ago
Pigeons can do better than humans when they work as a team: https://www.scientificamerican.com/article/using-pigeons-to-diagnose-cancer/ https://www.scientificamerican.com/article/using-pigeons-to-... but obviously no one is going to trust pigeon diagnosis anytime soon. Computers have a bit more credibility.
WhitneyLand 7y ago
I don't get the relevance of comments like this which I hear all the time. Say everything in the comment is true. It's all within the noise of a decade of research from now till 2030. The big picture is, this patchy performance is the writing is on the wall. It's over for radiologists, for the most part. The nature of this problem is a great fit for ML and it will in short order (10 years) be superior in the vast majority of scenarios to expert level humans. People say, but psychology, fear, unknowns, will require human supervision indefinitely. Of course that's true. The problem is, radiologists will effectively be relegated to proofreaders. The number of minutes required of them per patient will plummet and so will their job market (unless changes allow many more untreated people to get imaged). What about the researchers? Even they will take a hit as the imaging analysis part of radiology research moves more along the spectrum toward yet another computer science problem.
- module0000 7y ago
  > People say, but psychology, fear, unknowns, will require human supervision indefinitely. Of course that's true. The humans that supervise that will become the new radiologists. The "best" of those humans will have cross-field disciplines in ML model development and traditional radiology. My capitalist side sees a huge opportunity here in consulting and helping the existing radiology departments(who are interested) bridge the gap from current practice to a hybrid approach. Adapt or die, etc.
  - WhitneyLand 7y ago
    - What about the point that the total headcount demand for these supervisors/proofreaders will likely be drastically reduced? - The idea of cross-training radiologists to understand ML model development might be an ideal, but it's hard to see more than single digit percentages of folks who could make that transition. The jobs are way different and require years of ramp up. Even the mindset and the cultural approaches to work seems different, based on only a couple couple times I've worked on cross discipline teams. But hey, it's still unfolding, I could be wrong about the consulting. If you figure out a model and how to sell it ping me, I'd be glad to provide feedback to help you refine it quickly.
wjn0 7y ago
This is a crucial point. There's a good chance these are problems that could be solved with more data. However, I think philosophically the challenge of integrating these models in a modern healthcare system is more fundamentally related to explainability. A crucial part of a radiologist's job is the "why?" - why did you make this diagnosis, why did you flag this patient, etc. While there are models (especially in image processing) that tackle this problem, I'm not personally aware of them being used in a clinical setting. It's difficult from not only a machine learning perspective, but also in terms of HCI/UX.
taneq 7y ago
> The problem is that they fail differently than humans do, in a way that humans wind up not trusting the results. Not only that, but human fallibility is accepted where machine fallibility is not. There's something about being a "person" which makes it acceptable for you to just take the blame for something. A senior radiologist makes a glaring error and "it happens, people make mistakes". A computer makes the same error and it's a problem which must be fixed before the computer can be trusted. Ultimately I believe this is a cognitive bias that we're just going to have to learn to let go of.
- semi-extrinsic 7y ago
  > Ultimately I believe this is a cognitive bias that we're just going to have to learn to let go of. Unfortunately I don't think this is merely cognitive bias. It's actually built into our legal system at a pretty fundamental level: machines are held to a higher standard than humans when it comes to failures with grave consequences. And keep in mind, this system only achieves the same accuracy as doctors. What is the wnd benefit, other than shifting where money flows? Do you really think this benefit is substantial enough that we will se major overhauls of tort law in all US states and in every country in the rest of the world?
  - psychoslave 7y ago
    > Unfortunately I don't think this is merely cognitive bias. It's actually built into our legal system at a pretty fundamental level: machines are held to a higher standard than humans when it comes to failures with grave consequences. Well, laws reflect culturo-cognitive biases, don't they? And also they evolve. For the last part, that seems to generally be a wise "bias": when a machine fails, chances are good that all the identical machines will fail the same way. When a human err, chances are good that this won't be in a way you might expect to identically happen in all its peers, although common cultural biases can happen obviously. Also so far, our machine are far less expected to self-correct their behaviors when they err, especially regarding some untold social exceptions that were not met through some unexpected side effects. Ultimately, machines don't care of consequences of their acts because they don't have any feeling of responsibility to someone they love, not even themselves.
sadness2 7y ago
> humans wind up resenting the computer... basis for this assertion?

by btilly 7y ago

hathawsh 7y ago

That's a helpful perspective. Would it be possible for IBM to create a service that allows patients to submit their own pictures for scanning?

lostlogin 7y ago
We are at a tricky stage with this. Images are too big to be emailed and many people no longer have CD drives. There is increasing use of tomographic imaging in examinations too, and the files are pretty big.
- hathawsh 7y ago
  On the other hand, cheap micro SD cards and thumb drives hold far more than CDs or DVDs.
  - lostlogin 7y ago
    We prefer to send the images to the PACS the patient wants them on, as for most imaging that’s all that’s wanted. Obstetric imaging is one massive exception.
- toomuchtodo 7y ago
  Auth workflow so whomever does the imaging stores the image, and the patient can grant access and a pointer to systems consuming the imaging.

ska 7y ago

I worked on one of those systems. For some screening tasks, it has been at or beating average-radiologist performance since the mid or late 90s. There are a number of issues, but it's true that raw algorithmic performance is a small part of the whole picture.

RosanaAnaDana 7y ago
This is often the case in almost all ml-augmented workflows. The augmentation begins and ends with people looking at things, because the machine only knows what you told it, and doesn't care. The real work is building systems around people who do the interpretation and labeling to make their jobs easier.
- ska 7y ago
  Yes, very much this. And the importance of workflow impact cannot be overstated. The (clinical) system I mention above, the ML part was a few percent of the total effort, max.
agumonkey 7y ago
can you tell us more about the rest ?

cma 7y ago

> Thus when a human sees what the computer caught that the human did not, the mistake is easily dismissed. You mean dismissed as in not believing the result or not being impressive? (unclear if that's what you mean later by resenting and if it is tied to this statement)

btilly 7y ago
I mean dismissed as in not being impressive. "Yeah, yeah, I should have seen that. It is obvious. No big deal." And if you use the software as a backup, people don't respond well to their dumb mistakes being pointed out. And the result is that people put effort in to not making dumb mistakes...and therefore either slow down or have less time for what humans can do better than computers. In theory it should work a lot better than it actually does.
- inimino 7y ago
  What if you use the human as the backup? So they are looking for things the ML system missed. (As well as confirming the things it found.)

Symmetry 7y ago

So a lot like the centaur era in chess competitions.

twanvl 7y ago

> The problem is that they fail differently than humans do That is a great argument for giving such a model as an aid to a human doctor. Together they will be better then either one alone.

onlyrealcuzzo 7y ago
In Thinking, Fast and Slow -- the author details a double blind trial where the did this. It was worse with humans and AI than with just AI. Humans think they can use AI as a guide and move it in the right direction. But the movements they made, on average, were bad.
- notahacker 7y ago
  Surely in this type of instance (looking at a scan to answer a yes/no question) the human and AI act independently, with the computer being a useful aid because it separately picks up a few of the human's false negatives. Assuming false negatives are a lot worse than false positives, this can only be a good thing.
  - chongli 7y ago
    If they lead to an unnecessary mastectomy then false positives are pretty bad. Not as bad as dying, obviously, but still a severe blow to a woman's identity and sense of self worth. It's going to be a hard pill to swallow if you have to tell a woman "sorry, we removed your healthy breast because the computer made a mistake."
    - irq11 7y ago
      Even a false positive that leads to telling the patient that they may have cancer is bad. It leads to a life-long anxiety for many people.
    - glangdale 7y ago
      I think the idea of "screening" is that you don't just race off to a mastectomy the minute some AI model goes off. Of course, putting more false positives through a fallible process of review does run the risk you speak of.

benatkin 7y ago

I think this could be a new milestone, compared to what you saw, because it uses deep neural networks. I also am not sure saying computers can't catch the hard ones is true in light of this. It seems like a deep neural network would be useful to catch the hard ones. I agree with using the two together, but I don't see why the two can't be different AI subsystems. That seems to be what they're going for at IBM. Some of the power comes from a scientific model, while more power comes from well-trained deep learning networks. Before Alpha Zero, advances in self-driving cars, and face detection advances, I'd have agreed with you.

neaden 7y ago

Pigeons can do better than humans when they work as a team: https://www.scientificamerican.com/article/using-pigeons-to-diagnose-cancer/ https://www.scientificamerican.com/article/using-pigeons-to-... but obviously no one is going to trust pigeon diagnosis anytime soon. Computers have a bit more credibility.

WhitneyLand 7y ago

I don't get the relevance of comments like this which I hear all the time. Say everything in the comment is true. It's all within the noise of a decade of research from now till 2030. The big picture is, this patchy performance is the writing is on the wall. It's over for radiologists, for the most part. The nature of this problem is a great fit for ML and it will in short order (10 years) be superior in the vast majority of scenarios to expert level humans. People say, but psychology, fear, unknowns, will require human supervision indefinitely. Of course that's true. The problem is, radiologists will effectively be relegated to proofreaders. The number of minutes required of them per patient will plummet and so will their job market (unless changes allow many more untreated people to get imaged). What about the researchers? Even they will take a hit as the imaging analysis part of radiology research moves more along the spectrum toward yet another computer science problem.

module0000 7y ago
> People say, but psychology, fear, unknowns, will require human supervision indefinitely. Of course that's true. The humans that supervise that will become the new radiologists. The "best" of those humans will have cross-field disciplines in ML model development and traditional radiology. My capitalist side sees a huge opportunity here in consulting and helping the existing radiology departments(who are interested) bridge the gap from current practice to a hybrid approach. Adapt or die, etc.
- WhitneyLand 7y ago
  - What about the point that the total headcount demand for these supervisors/proofreaders will likely be drastically reduced? - The idea of cross-training radiologists to understand ML model development might be an ideal, but it's hard to see more than single digit percentages of folks who could make that transition. The jobs are way different and require years of ramp up. Even the mindset and the cultural approaches to work seems different, based on only a couple couple times I've worked on cross discipline teams. But hey, it's still unfolding, I could be wrong about the consulting. If you figure out a model and how to sell it ping me, I'd be glad to provide feedback to help you refine it quickly.

wjn0 7y ago

This is a crucial point. There's a good chance these are problems that could be solved with more data. However, I think philosophically the challenge of integrating these models in a modern healthcare system is more fundamentally related to explainability. A crucial part of a radiologist's job is the "why?" - why did you make this diagnosis, why did you flag this patient, etc. While there are models (especially in image processing) that tackle this problem, I'm not personally aware of them being used in a clinical setting. It's difficult from not only a machine learning perspective, but also in terms of HCI/UX.

taneq 7y ago

> The problem is that they fail differently than humans do, in a way that humans wind up not trusting the results. Not only that, but human fallibility is accepted where machine fallibility is not. There's something about being a "person" which makes it acceptable for you to just take the blame for something. A senior radiologist makes a glaring error and "it happens, people make mistakes". A computer makes the same error and it's a problem which must be fixed before the computer can be trusted. Ultimately I believe this is a cognitive bias that we're just going to have to learn to let go of.

semi-extrinsic 7y ago
> Ultimately I believe this is a cognitive bias that we're just going to have to learn to let go of. Unfortunately I don't think this is merely cognitive bias. It's actually built into our legal system at a pretty fundamental level: machines are held to a higher standard than humans when it comes to failures with grave consequences. And keep in mind, this system only achieves the same accuracy as doctors. What is the wnd benefit, other than shifting where money flows? Do you really think this benefit is substantial enough that we will se major overhauls of tort law in all US states and in every country in the rest of the world?
- psychoslave 7y ago
  > Unfortunately I don't think this is merely cognitive bias. It's actually built into our legal system at a pretty fundamental level: machines are held to a higher standard than humans when it comes to failures with grave consequences. Well, laws reflect culturo-cognitive biases, don't they? And also they evolve. For the last part, that seems to generally be a wise "bias": when a machine fails, chances are good that all the identical machines will fail the same way. When a human err, chances are good that this won't be in a way you might expect to identically happen in all its peers, although common cultural biases can happen obviously. Also so far, our machine are far less expected to self-correct their behaviors when they err, especially regarding some untold social exceptions that were not met through some unexpected side effects. Ultimately, machines don't care of consequences of their acts because they don't have any feeling of responsibility to someone they love, not even themselves.

sadness2 7y ago

> humans wind up resenting the computer... basis for this assertion?