AI models that predict disease are not as accurate as reports might suggest • Hacker News Board

Re: AI models that predict disease are not as accurate as reports might suggest

#41

Post by dirheist » Fri, Oct 21, 2022, 7:59 PM UTC

planetsprite wrote: post #19

The solution to failures of AI in heathcare is transparency of data. OpenAI's models work because they have virtually unlimited data to train on. The scale of training data for doctor bots is one millionth the size. Different countries, organizations, universities need to be as open as possible sharing and collaborating, realizing improvements in medicine benefits all of humanity with almost no downsides.

There should be a standardization committee tasked with standardizing the collection of anonymized, semi-synthetic medical data from hospitals/hospital networks. It seems like so much research is just locked up in the IMS systems the hospitals use for their patients and that never see the light of day.

Re: AI models that predict disease are not as accurate as reports might suggest

#42

Post by rjdagost » Fri, Oct 21, 2022, 8:00 PM UTC

tensor wrote: post #15

This is entirely unsurprising and has a very simple solution: keep adding more data. Our measurements of the accuracy of AI systems are only as good as the test data, and if the test data is too small, then the reported accuracies won't reflect the true accuracies of the model applied to wild data. Basically, we need an accurate measure of whether the test data set is statistically representative of wild data. In hea…

If there's one thing I learned with biomedical data modeling and machine learning, it's that "it's complicated". For biomedical scenarios, getting more data is often not simple at all. This is especially the case for rare diseases. For areas like drug discovery, getting a single new data point (for example, the effect of a drug candidate in human clinical settings) may require a huge expenditure of time and money. Biomedical results are often plagued with confounding variables, hidden and invisible, and simply adding in more data without detection and consideration of these bias sources can be disastrous. For example, measurements from lab #1 may show persistent errors not present in lab #2, and simply adding in more data blindly from lab #1 can make for worse models.

My conclusion is that you really need domain knowledge to know if you're fooling yourself with your great-looking modeling results. There's no simple statistical test to tell you if your data is acceptable or not.

Re: AI models that predict disease are not as accurate as reports might suggest

#43

Post by ben_w » Fri, Oct 21, 2022, 8:03 PM UTC

junipertea wrote: post #24

kbenson wrote: post #13

Earlier quoted context omitted.

I don't know, compassion and understanding and nuanced understanding of individual desires when talking to someone is not what I associate AI with in my mind, but being able to assess sociological and cultural taboos and try to what a patient actually wants rather then what they might initially express seems like something I good doctor would get to through explorative conversation.

Maybe removing a human from the equation would lead to more honest outcome? E.g. people google all sorts of issues more earnestly than they would describe it to the doctors. The bottleneck would be properly understanding what the user intends, which might be out of reach.

Indeed. Language has been historically difficult for AI, but I think it's even tougher here — language is less and less reliable the further we get from a shared experience, and this is a problem when describing our experiences of our own bodies, and much worse when describing our own minds.

For example, when I was coming off an SSRI, I was forewarned that I might get a sensation of "electric shocks"; the actual experience wasn't like that, though I could tell why they chose to describe it like that.

How different is the tightness in the chest during a heart attack from the tightness in the chest from exercising chest muscles?

I have no idea how doctors, GPs, and nurses manage this, though they seem to have relatively little trouble.

Re: AI models that predict disease are not as accurate as reports might suggest

#44

Post by chatterhead » Fri, Oct 21, 2022, 8:05 PM UTC

ckemere wrote: post #27

bpodgursky wrote: post #10

Earlier quoted context omitted.

It baffles me that people can watch the trendline of "Job X can be automated in 40 years" (5 years ago) "Job X can be automated in 10 years" (2 years ago) "Job X can be automated in 5 years" (1 week ago) And feel comfortable poking holes in the AI models, pointing out where it fails. Obviously? But nobody 3 years ago thought that graphic design or creative writing was on death's row either. You have to spend a modicu…

There's also the timeline that: "Radiology will be automatized in 5 years" (10 years ago) "Radiology will be automatized in 5 years" (5 years ago) "Radiology will be automatized in 5 years" (last year) or "Full self driving will arrive within 5 years" (5 years ago) "Full self driving is still a ways off" (last year) Assuming you're referring to generative models, I don't think that anyone (knowledgable) thinks that g…

We are 18 years from the DARPA Grand Challenge and none of the vehicles finished.

Do you think a self-driving car can make it from LA to NYC by itself now?

What do you think 2040 AI will look like?

Re: AI models that predict disease are not as accurate as reports might suggest

#45

Post by srvmshr » Fri, Oct 21, 2022, 8:07 PM UTC

dekhn wrote: post #38

srvmshr wrote: post #34

I worked in healthcare ML solutions, as part of my PhD & also as consultant to a telemedicine company. My experience in dealing with data (we had sufficient, and somewhat well labeled) & methods made me realize that a lot of the prediction human doctors make are multimodal - and that is something deep learning will struggle for the time being. For example, say in detection of a disease X , physicians factor in blood…

are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.

> Are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better?

Personal story: I was diagnosed with a rare genetic disease in 2019. If I ran the symptoms through a ML gauntlet, I would be sure they would cancel each other out or make little sense. Chest CT (clean), fever (high), TB test (negative), latent TB marker (positive), vision difficulty (Nothing unusual yet), edema in eye socket (yes), WBC count (normal), tumors (none), hormones (normal) & retina images (severely abnormal)

My condition was zeroed in within 5 minutes of a visitation to a top retina specialist, after regular opthalmologists were in a fix about two conflicting conditions. This was differential diagnosis based even though genetic assay hadn't returned yet, which also later came in favor. I cannot overemphasize enough how good human brain is in recalling information & connecting the sparse dots to logical conclusions

(I am one of 0.003% unlucky ones among all opthalmological cases & the only active patient with that affliction in one of the busiest hospitals in the country. My data is part of the 36 people in a NIH study & opthalmo residents are routinely called in to see me as case study when I go for follow up quarterly).

Re: AI models that predict disease are not as accurate as reports might suggest

#46

Post by lostlogin » Fri, Oct 21, 2022, 8:08 PM UTC

ckemere wrote: post #27

bpodgursky wrote: post #10

Earlier quoted context omitted.

It baffles me that people can watch the trendline of "Job X can be automated in 40 years" (5 years ago) "Job X can be automated in 10 years" (2 years ago) "Job X can be automated in 5 years" (1 week ago) And feel comfortable poking holes in the AI models, pointing out where it fails. Obviously? But nobody 3 years ago thought that graphic design or creative writing was on death's row either. You have to spend a modicu…

There's also the timeline that: "Radiology will be automatized in 5 years" (10 years ago) "Radiology will be automatized in 5 years" (5 years ago) "Radiology will be automatized in 5 years" (last year) or "Full self driving will arrive within 5 years" (5 years ago) "Full self driving is still a ways off" (last year) Assuming you're referring to generative models, I don't think that anyone (knowledgable) thinks that g…

Having seen some of the automation available in radiology, I’m a bit baffled as to why I still have a job as an MRI tech.

5 years ago I watched automated cardiac MRI, and it worked well. I was told about a site that were having good results with fetal cardiac MRI via a related bit of software.

These scans are hard to do, and the machines did well. In some cases they got confused and did a good functional analysis but of the stomach, not the heart. Oops, but easily fixed by almost anyone after a few minutes of explanation.

Why are basic MSK scans still done by a tech with years of training?

I don’t know the answer to that as it’s basic stuff and if I end my career without machines having taken over the basic stuff, I’ll be a bit disappointed.

Re: AI models that predict disease are not as accurate as reports might suggest

#47

Post by rscho » Fri, Oct 21, 2022, 8:13 PM UTC

Surprise, surprise. People hugely overestimate the data retrieval capabilities of healthcare systems. And if you really put clinical 'AI' systems to the test in day-to-day settings (which is in fact never done), results would be much, much worse.

Shit data in, shit prediction out.

Re: AI models that predict disease are not as accurate as reports might suggest

#48

Post by tappio » Fri, Oct 21, 2022, 8:21 PM UTC

dirheist wrote: post #39

spywaregorilla wrote: post #22

Earlier quoted context omitted.

That's not a contradiction per se. It's easier to get spurriously high test scores with smaller datasets. It does not clearly demonstrate that the models are actually getting worse.

But if diagnosis are multimodal and rely upon large, multidimensional analysis of symptoms/bloodwork/past medical history, wouldn't adding more dimensions just increase dimensional sparsity and decrease the useful amount of conclusions you are able to draw from your variables? It's been a long time since I remember learning about the curse of dimensionality but if you increase the amount of datapoints you collect by…

You are right, but I feel you misunderstood op.

I understood that op meant increase number of samples, not variables.

Re: AI models that predict disease are not as accurate as reports might suggest

#49

Post by ggm » Fri, Oct 21, 2022, 8:30 PM UTC

lostlogin wrote: post #32

dm319 wrote: post #7

As someone who works in healthcare, so much of what I read about AI makes me think that the people who are enthusiastic about healthcare AI don't have much experience doing it. The scenarios rarely seem to fit with what I'm actually practicing. Most of medicine is boring, it is largely routine, and if we don't know what's going on, it's because we're not the right person to be managing the patient. Most of my time is…

I work in radiology with MRI as a tech. We use AI slightly differently to the examples here, but it’s changing a lot of what we do. It’s more about enhancing images than directly about diagnosing. The image is denoised ‘intelligently’ in k-space and then the resolution is doubled via another AI process in the image domain (or maybe the resolution is quadrupled, as it depends on how you measure it. Our pixel count dou…

My partner had a clinician review her paperwork and say "why are you here" explaining the enhanced imaging was leading to tentative concerns being raised about structural change so small it was below the threshold for safe surgical treatment.

Moral of the story: the imaging has got so good that diagnostics is now on the fringe of over diagnosing and the stats need to catch up

Re: AI models that predict disease are not as accurate as reports might suggest

#50

Post by majormajor » Fri, Oct 21, 2022, 8:32 PM UTC

dekhn wrote: post #38

srvmshr wrote: post #34

I worked in healthcare ML solutions, as part of my PhD & also as consultant to a telemedicine company. My experience in dealing with data (we had sufficient, and somewhat well labeled) & methods made me realize that a lot of the prediction human doctors make are multimodal - and that is something deep learning will struggle for the time being. For example, say in detection of a disease X , physicians factor in blood…

are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.

My view on this is framed a bit differently but probably a similar ultimate perspective:

I think it's probably going to be a long time before models only using quantifiable measurements can even meet the performance of top doctors. I can't recommend enough that someone experiencing issues doctor-shop if they haven't gotten a well-explained diagnosis from their current doctor.

But I'm very curious how good one has to be in order to be better than a below-average doctor, or a 50th-percentile doctor, or a 75th...

But I also think there may be weird failure modes similar to today's not-fully-self-driving cars along the lines of "if even the 75th-percentile-doctor uses the tool and sees an output that stops them from asking a question they otherwise might have, can it hurt things too?"