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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

#21

Post by eduramiba »

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.

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/

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

#22

Post by rdedev »

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…

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)

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

#23

Post by rdedev »

klodolph wrote: post #16
gjulianm wrote: post #14

Earlier quoted context omitted.

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.

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.

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

#24

Post by esyir »

klodolph wrote: post #16
gjulianm wrote: post #14

Earlier quoted context omitted.

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.

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.

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

#25

Post by exporectomy »

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…

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.

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

#26

Post by dvfjsdhgfv »

klodolph wrote: post #16
gjulianm wrote: post #14

Earlier quoted context omitted.

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.

Where I live it's not uncommon for doctors to consult the images with their colleagues whenever they come across something unusual, even if they are "pretty sure" of their diagnosis, because the consequences of an error can be severe - we're talking literally about life and death here. So the ML system would have to be as precise as 2-3 humans together in order to be considered "good enough".

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

#27

Post by axelroze »

spoonjim wrote: post #20

> 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.

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.

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

#28

Post by ChuckNorris89 »

axelroze wrote: post #27
spoonjim wrote: post #20

> 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.

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.

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.

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

#29

Post by axelroze »

beforeolives wrote: post #19
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.

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?

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

#30

Post by axelroze »

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...

A friend of mine once got rejected from a big name company after a few months of interviews. Reason was that he did not have a PhD. It's hard to get into the business.

You could try to start-up and then plan to get acquihired. Or go and do a PhD. It's never too late for more schooling.

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