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

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

#11

Post by tgv »

gjulianm wrote: post #8

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…

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.

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

#13

Post by bayesian_horse »

BiteCode_dev wrote: post #10

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.

Medical ML is not about making decisions but about informing decisions. It is a tool for the doctor, not a replacement.

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

#14

Post by gjulianm »

tgv wrote: post #11
gjulianm wrote: post #8

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…

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.

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?

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

#15

Post by ecmascript »

bayesian_horse wrote: post #12

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...

Why not build something on your spare time and try to create your own job then?

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

#16

Post by klodolph »

gjulianm wrote: post #14
tgv wrote: post #11

Earlier quoted context omitted.

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.

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?

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.

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

#17

Post by BiteCode_dev »

bayesian_horse wrote: post #13
BiteCode_dev wrote: post #10

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.

Medical ML is not about making decisions but about informing decisions. It is a tool for the doctor, not a replacement.

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.

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

#18

Post by klodolph »

> 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.

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

#19

Post by beforeolives »

debarshri wrote: post #2

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.

I have geniunely considered switching away from data science altogether for this reason.

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

#20

Post by spoonjim »

> 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.

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