5 ms·

This type of evaluation (both the meta-evaluation and the underlying studies themselves) are the exact sort things AI enthusiasts should want researchers to do.

This type of evaluation (both the meta-evaluation and the underlying studies themselves) are the exact sort things AI enthusiasts should want researchers to do. It cuts through the hype, and gives everyone a clear assessment of where the technology currently stands. Clearly there is room for improvement. Maybe this study will also spur the development of new types of systems which augment human/radiologist decision-making.

snarf21 5y ago
AI isn't a stand-alone panacea for this. We've done lost of studies with other radiologists. Taking the union of two "bad" radiologists outperforms a single "good" radiologist. Humans aren't machines, we have bad days. I think the long term benefit of this kind of AI is as an audit AFTER the radiologist has done their assessment. If there is a mismatch, it should trigger a follow-up with a different a radiologist (without telling them the result of the 1st or the AI) to build consensus.
- ajmurmann 5y ago
  This! I wish we did this type of usage a lot more in a variety of areas. I previously proposed this at a job to enhance filtering out of false-positive security scan results. I think it doesn't get used because it would increase the immediate cost, not reduce it.
  - tsjq 5y ago
    >I think it doesn't get used because it would increase the immediate cost, not reduce it. indeed that is the problem in many areas. Almost always mgmt looks with "tech = fewer human = instant cost savings" expectations. and anything that increases cost in the short term are shot down. sad . all blame on the cost-center vs profit-center mindset.
- spockz 5y ago
  I think getting a second “opinion” is pretty standard for biopsies?
- Aspos 5y ago
  AI which is slightly less effective than a well-educated, well-trained, expensive radiologist is better than nothing. There are places in this world which could afford a $XXK x-ray machine, but don't have access to a good radiologist.
- YossarianFrPrez 5y ago
  I completely agree. In fact, one wonders if the biggest use of AI/ML in medicine will actually be in medical notes and record keeping -- voice transcriptions, medical scribing, and interpreting writing, free-form text into standard diagnostic categories.
- divbzero 5y ago
  Agreed! It’s easy to get sucked into the (intriguing) human vs. machine question and forget that the best solution is often both or some orthogonal improvement.
colechristensen 5y ago
What seems to be coming up repeatedly is that machine learning techniques are pretty good at being almost adequate replacements for recognition problems. Able to catch blunders but also missing at a rate high enough that you need the expert human anyway. In reality just replacing human kinds of error with machine kinds of error. Automation though can also make experts stupid depriving them of the practice to keep skills sharp.
- randcraw 5y ago
  That's why most uses of AI in medicine that I've seen have focused on performing a sanity check on the physician (e.g. checking a new prescription for contraindications like conflicting meds or maladies). Medical insurance companies have embraced this practice for obvious economic reasons.
- 256lie 5y ago
  It's known as automation bias and a problem in pilots as well as doctors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/
bakuninsbart 5y ago
Doing this early also helps people get used to the idea of AI replacing/complementing doctors in the future. Objectively, it would be better to be diagnosed by a machine instead of a human doctor, if the machine is better on average at giving the right diagnosis. In practice, there is a large legal and emotional gap to close to get acceptance for this. The gains could be incredible though, not only would we get better diagnosis, it could be done faster, cheaper, and remotely.
- cratermoon 5y ago
  > Objectively, it would be better to be diagnosed by a machine instead of a human doctor, if the machine is better on average at giving the right diagnosis. "On average"? What if the machine is better than average for common things but consistently, 100%, misses uncommon conditions with very high short-term mortality rates?
TuringNYC 5y ago
My first step in doing such a meta-study would be to throw away any paper with <1000 (or choose your threashold) training sets. If the n value is insufficient to train the number of parameters on the model, the paper isn't good. When I was CTO of DocHuddle (ML + Radiology), we reviewed every paper and preprint we could find and a huge number appeared to be overfit on tiny datasets. Any meta-study which doesnt throw away obviously poor attempts will find bad-skewed meta-findings.
- YossarianFrPrez 5y ago
  That's a real shame. It's been years since "The Unreasonable Effectiveness of Data" has been published and out in the world. You'd think that using a large N for statistical learning methodologies would be standard practice by now. Do Hospitals keep radiological images? Are they researching this stuff? Presumably they'd have a large enough sample size.
  - bgandrew 5y ago
    Actually, a real shame is a total lack of really big (millions of records) data sets on pretty much anything medical. And this is of course due to our love with privacy. Probably millions of people died too soon because of that, and many more will follow in the future.
    - TuringNYC 5y ago
      I'd agree that millions suffer due to a lack of care, or a lack of affordable care, or a lack of early diagnosis. I'd disagree this is due to privacy. IMHO it is due to conflicts of interest in major medical systems (certainly in the US) where incentives are skewed away from efficiency towards more billing.
  - TuringNYC 5y ago
    Yes, hospitals keep imaging on their PACS systems and VNAs (Vendor Neutral Archives). Large systems have millions of images, though not necessarily same body part. Very large systems have millions of the same body part alone. Getting images is one thing. Getting labels is harder. Getting annotations on regions-of-interest is yet harder. Often the labels are stuck in unstructured data (notes.) When I was doing my startup (https://www.dochuddle.com/ https://www.dochuddle.com/) we trained our classifier and object detector on 1.2 million images. We worked with a large sovereign on getting the images, reports, and annotations. Just dealing with 20+ TB of images was a job in its-self...but it was indeed unreasonably effective. The success was only technical and ML success. Even harder -- commercializing it effectively after accounting for legal/contract costs. Yet harder -- getting past conflicts of interest inherent in the US medical system. We were not successful on this front. Possibly too early (we started in 2014)
256lie 5y ago
Meta analysis is more common in medical journals than computer science conferences. The evaluation of AI medical devices is determined by regulatory agencies like the FDA.
FiberBundle 5y ago
I'm also generally sceptical of the current AI hype, but I don't see exactly how these results cut through the hype. Why isn't it seen as an accomplishment that 6% of those systems performed better than an actual radiologist?
tialaramex 5y ago
I remember a friend in an allied discipline talking to me about what they did for cervical cancer where again the initial AI outcomes are weak. I don't know if this is now gold standard clinical practice, or still an experimental, but here's how I remember it: We screen people with a cervix for cancer by scraping away a tiny sample of the cells periodically and having that examined at a laboratory for anomalies. If caught early, cervical cancer isn't fun but it's extremely survivable. You don't technically need a cervix (after all about 50% of humans don't have one) so in the worst case hysterectomy (removal of the womb and cervix) is an option. Machines aren't very good at looking at a slide full of cells from the human cervix and giving it a score like 1-5 where 1 is "Fine" and 5 is "Cancer". This is a standardised task that her (human) team do every day, and she works with other bodies across the continent to ensure they're all doing a roughly similar job by looking at each others examples and checking they get the same number, so this way a doctor in Paris and one in Birmingham should get interchangeable results despite using different labs to process the samples. However, it turns out the machines are stellar at a related task. "Is this sample infected with HPV?". Humans do not find this task easy and historically this test wasn't done anyway. So maybe a human would do the first thing, and, if it was a bit borderline, then they'd check the second thing. Cervical cancer is almost always caused by HPV, so if you don't have HPV then you almost certainly don't have cervical cancer. But since the machines are great at that second problem you can reverse things. The machine processes every sample for HPV and then a human only looks at the positive ones, rating those.

snarf21 5y ago

AI isn't a stand-alone panacea for this. We've done lost of studies with other radiologists. Taking the union of two "bad" radiologists outperforms a single "good" radiologist. Humans aren't machines, we have bad days. I think the long term benefit of this kind of AI is as an audit AFTER the radiologist has done their assessment. If there is a mismatch, it should trigger a follow-up with a different a radiologist (without telling them the result of the 1st or the AI) to build consensus.

ajmurmann 5y ago
This! I wish we did this type of usage a lot more in a variety of areas. I previously proposed this at a job to enhance filtering out of false-positive security scan results. I think it doesn't get used because it would increase the immediate cost, not reduce it.
- tsjq 5y ago
  >I think it doesn't get used because it would increase the immediate cost, not reduce it. indeed that is the problem in many areas. Almost always mgmt looks with "tech = fewer human = instant cost savings" expectations. and anything that increases cost in the short term are shot down. sad . all blame on the cost-center vs profit-center mindset.
spockz 5y ago
I think getting a second “opinion” is pretty standard for biopsies?
Aspos 5y ago
AI which is slightly less effective than a well-educated, well-trained, expensive radiologist is better than nothing. There are places in this world which could afford a $XXK x-ray machine, but don't have access to a good radiologist.
YossarianFrPrez 5y ago
I completely agree. In fact, one wonders if the biggest use of AI/ML in medicine will actually be in medical notes and record keeping -- voice transcriptions, medical scribing, and interpreting writing, free-form text into standard diagnostic categories.
divbzero 5y ago
Agreed! It’s easy to get sucked into the (intriguing) human vs. machine question and forget that the best solution is often both or some orthogonal improvement.

colechristensen 5y ago

What seems to be coming up repeatedly is that machine learning techniques are pretty good at being almost adequate replacements for recognition problems. Able to catch blunders but also missing at a rate high enough that you need the expert human anyway. In reality just replacing human kinds of error with machine kinds of error. Automation though can also make experts stupid depriving them of the practice to keep skills sharp.

randcraw 5y ago
That's why most uses of AI in medicine that I've seen have focused on performing a sanity check on the physician (e.g. checking a new prescription for contraindications like conflicting meds or maladies). Medical insurance companies have embraced this practice for obvious economic reasons.
256lie 5y ago
It's known as automation bias and a problem in pilots as well as doctors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/

bakuninsbart 5y ago

Doing this early also helps people get used to the idea of AI replacing/complementing doctors in the future. Objectively, it would be better to be diagnosed by a machine instead of a human doctor, if the machine is better on average at giving the right diagnosis. In practice, there is a large legal and emotional gap to close to get acceptance for this. The gains could be incredible though, not only would we get better diagnosis, it could be done faster, cheaper, and remotely.

cratermoon 5y ago
> Objectively, it would be better to be diagnosed by a machine instead of a human doctor, if the machine is better on average at giving the right diagnosis. "On average"? What if the machine is better than average for common things but consistently, 100%, misses uncommon conditions with very high short-term mortality rates?

TuringNYC 5y ago

My first step in doing such a meta-study would be to throw away any paper with <1000 (or choose your threashold) training sets. If the n value is insufficient to train the number of parameters on the model, the paper isn't good. When I was CTO of DocHuddle (ML + Radiology), we reviewed every paper and preprint we could find and a huge number appeared to be overfit on tiny datasets. Any meta-study which doesnt throw away obviously poor attempts will find bad-skewed meta-findings.

YossarianFrPrez 5y ago
That's a real shame. It's been years since "The Unreasonable Effectiveness of Data" has been published and out in the world. You'd think that using a large N for statistical learning methodologies would be standard practice by now. Do Hospitals keep radiological images? Are they researching this stuff? Presumably they'd have a large enough sample size.
- bgandrew 5y ago
  Actually, a real shame is a total lack of really big (millions of records) data sets on pretty much anything medical. And this is of course due to our love with privacy. Probably millions of people died too soon because of that, and many more will follow in the future.
  - TuringNYC 5y ago
    I'd agree that millions suffer due to a lack of care, or a lack of affordable care, or a lack of early diagnosis. I'd disagree this is due to privacy. IMHO it is due to conflicts of interest in major medical systems (certainly in the US) where incentives are skewed away from efficiency towards more billing.
- TuringNYC 5y ago
  Yes, hospitals keep imaging on their PACS systems and VNAs (Vendor Neutral Archives). Large systems have millions of images, though not necessarily same body part. Very large systems have millions of the same body part alone. Getting images is one thing. Getting labels is harder. Getting annotations on regions-of-interest is yet harder. Often the labels are stuck in unstructured data (notes.) When I was doing my startup (https://www.dochuddle.com/ https://www.dochuddle.com/) we trained our classifier and object detector on 1.2 million images. We worked with a large sovereign on getting the images, reports, and annotations. Just dealing with 20+ TB of images was a job in its-self...but it was indeed unreasonably effective. The success was only technical and ML success. Even harder -- commercializing it effectively after accounting for legal/contract costs. Yet harder -- getting past conflicts of interest inherent in the US medical system. We were not successful on this front. Possibly too early (we started in 2014)

256lie 5y ago

Meta analysis is more common in medical journals than computer science conferences. The evaluation of AI medical devices is determined by regulatory agencies like the FDA.

FiberBundle 5y ago

I'm also generally sceptical of the current AI hype, but I don't see exactly how these results cut through the hype. Why isn't it seen as an accomplishment that 6% of those systems performed better than an actual radiologist?

tialaramex 5y ago

I remember a friend in an allied discipline talking to me about what they did for cervical cancer where again the initial AI outcomes are weak. I don't know if this is now gold standard clinical practice, or still an experimental, but here's how I remember it: We screen people with a cervix for cancer by scraping away a tiny sample of the cells periodically and having that examined at a laboratory for anomalies. If caught early, cervical cancer isn't fun but it's extremely survivable. You don't technically need a cervix (after all about 50% of humans don't have one) so in the worst case hysterectomy (removal of the womb and cervix) is an option. Machines aren't very good at looking at a slide full of cells from the human cervix and giving it a score like 1-5 where 1 is "Fine" and 5 is "Cancer". This is a standardised task that her (human) team do every day, and she works with other bodies across the continent to ensure they're all doing a roughly similar job by looking at each others examples and checking they get the same number, so this way a doctor in Paris and one in Birmingham should get interchangeable results despite using different labs to process the samples. However, it turns out the machines are stellar at a related task. "Is this sample infected with HPV?". Humans do not find this task easy and historically this test wasn't done anyway. So maybe a human would do the first thing, and, if it was a bit borderline, then they'd check the second thing. Cervical cancer is almost always caused by HPV, so if you don't have HPV then you almost certainly don't have cervical cancer. But since the machines are great at that second problem you can reverse things. The machine processes every sample for HPV and then a human only looks at the positive ones, rating those.