40 ms·

I'll be finishing my interventional radiology fellowship this year. I remember in 2016 when Geoffrey Hinton said, "We should stop training radiologists now," th

I'll be finishing my interventional radiology fellowship this year. I remember in 2016 when Geoffrey Hinton said, "We should stop training radiologists now," the radiology community was aghast and in-denial. My undergrad and masters were in computer science, and I felt, "yes, that's about right." If you were starting a diagnostic radiology residency, including intern year and fellowship, you'd just be finishing now. How can you really think that "computers can't read diagnostic images" if models such as this can describe a VGA connector outfitted with a lighting cable?

dpflan 4y ago
What is your take then on how this affect your field? And your occupation? Do you think you will incorporate such technology into your day-to-day?
- aabajian 4y ago
  I think it will be radiologists signing-off auto-generated reports, with less reimbursement per study. It'll likely result in more work for diagnostic radiologists to maintain their same salary levels.
  - haldujai 4y ago
    It will take a very long time for this to happen, probably decades. Cardiologists are still paid to finalize ECG reports 3 days after a STEMI. I've worked at places with AI/CAD for lung nodules, mammo and stroke and there isn't even a whisper at cutting fee codes because of AI efficiency gains at the moment. N.B. I say this as a radiologist who elected not to pursue an interventional fellowship because I see reimbursement for diagnostic work skyrocketing with AI due to increases in efficiency and stagnant fee codes.
  - reubens 4y ago
    It’s hard to imagine this not happening in the next five years. Just depends on who is prepared to take on the radiologists to reduce their fee codes. Speaking as 2nd year radiology resident in Australia
sinuhe69 4y ago
It all comes down to labelled data. There are millions images of VGA connectors and lightning cables on the internet with description, where CLIP model and similar could learn to recognize them relatively reliably. On the other hand, I'm not sure such amount of data are available for AI training. Especially if the diagnostic is blinded, it will be even harder for the AI model to reliably differentiate between them, making cross-disease diagnostic hard. Not to mention the risk and reliability of such tasks.
- bick_nyers 4y ago
  As someone who has worked at a Radiology PACS with petabytes of medical images under management, this is 100% accurate. You might have images, but not the diagnoses to train the AI with. In addition, there are compliance reasons, just because you manage that data doesn't mean that you can train an AI on it and sell it, unless of course you get explicit permission from every individual patient (good luck). I do believe that with enough effort we could create AI specialist doctors, and allow the generalist family doctor to make a comeback, augmented with the ability to tap into specialist knowledge. Technology in the medical industry is extremely far behind modern progress though, CT images are still largely 512 by 512 pixels. It's too easy to get bogged down with legacy support to make significant advancements and stay on the cutting edge.
  - in3d 4y ago
    Seems like this is where centralized countries like China can get a significant edge over the U.S.
  - gwern 4y ago
    > You might have images, but not the diagnoses to train the AI with. That's what the unsupervised learning is for. GPT doesn't have labels either, just raw data.
    - rjtavares 4y ago
      There isn't an obvious unsupervised problem to train medical imaging with. What's the medical imaging equivalent to "predict the next word"?
hospitalJail 4y ago
If you are in the US. It is more important to have the legal paperwork, than to be factually correct. The medical cartels always will get their cut.
- ChickenNugger 4y ago
  I'm curious who the medical cartels in this context. Can you elaborate?
  - hospitalJail 4y ago
    See for yourself https://www.opensecrets.org/federal-lobbying/top-spenders?cycle=a https://www.opensecrets.org/federal-lobbying/top-spenders?cy...
- barking_biscuit 4y ago
  username checks out
  - pmoriarty 4y ago
    Please don't do this on HN.
    - barking_biscuit 4y ago
      Okay
  - Hoyadonis 4y ago
    [flagged]
    - barking_biscuit 4y ago
      Okay
- bpodgursky 4y ago
  Eventually it's going to be cheap enough to drop by Tijuana for $5 MRI that even the cartel has to react. Also, even within the US framework, there's pressure. A radiologist can rubberstamp 10x as many reports with AI-assistance. That doesn't eliminate radiology, but it eliminates 90% of the radiologists we're training.
hot_gril 4y ago
In 2015, I took an intro cognitive science class in college. The professor listed some natural language feats that he was certain AI would never accomplish. It wasn't long before average people were using AI for things he predicted were impossible.
haldujai 4y ago
As another radiologist, I'm not sure how you can say this with a straight face? If anything the minimal progress that has been made since Hinton made this claim should be encouraging people to pursue radiology training. As with other areas of medicine that have better AI (interpreting ECGs for example) all this will do is make our lives easier. AI is not an existential threat to radiology (or pathology for that matter which is an easier problem to solve than medical imaging). 1. Radiology =/= interpreting pixels and applying a class label. 2. Risk and consequences of misclassifying T-staging of a cancer =/= risk of misclassifying a VGA connector. 3. Imaging appearance overlap of radiological findings >>>>>>>>>> imaging appearance overlap of different types of connectors (e.g. infection and cancer can look the same, we make educated guesses on a lot of things considering many patient variables, clinical data, and prior imaging.) You would need to have a multi-modal model enriched with a patient knowledge graph to try and replicate this, while problems like this are being worked on we are no where close enough for this to be a near-term threat. We haven't even solved NLP in medicine, let alone imaging interpretation! 4. Radiologists do far more than interpret images, unless you're in a tele-radiology eat-what-you-kill sweatshop. This includes things like procedures (i.e. biopsies and drainages for diagnostic rads) and multidisciplinary rounds/tumor boards.
- aabajian 4y ago
  I agree that the level of risk/consequence is higher for radiology misses, but I wonder if radiologists are already missing things because of simplification for human feasibility. Things like LI-RADS and BI-RADS are so simple from a computer science perspective. I wouldn't even call them algorithms, just simple checkbox decision making. This tendency to simplify is everywhere in radiology: When looking for a radial head fracture, we're taught to exam the cortex for discontinuities, look for an elbow joint effusion, evaluate the anterior humeral line, etc. But what if there's some feature (or combination of feature) that is beyond human perception? Maybe the radial ulnar joint space is a millimeter wider than it should be? Maybe soft tissues are just a bit too dense near the elbow? Just how far does the fat pad have to be displaced to indicate an effusion? Probably the best "decision function" is a non-linear combination of all these findings. Oh, but we only have 1 minute to read the radiograph and move on to the next one. Unfortunately, as someone noted below, advances in medicine are glacially slow. I think change is only going to come in the form of lawsuits. Imagine a future where a patient and her lawyer can get a second-opinion from an online model, "Why did you miss my client's proximal scaphoid fracture? We uploaded her radiographs and GPT-4 found it in 2 seconds." If and when these types of lawsuits occur, malpractice insurances are going to push for radiologists to use AI. Regarding other tasks performed by radiologists, some radiologists do more than dictate images, but those are generally the minority. The vast majority of radiologists read images for big money without ever meeting the patient or the provider who ordered the study. In the most extreme case, radiologists read studies after the acute intervention has been performed. This happens a lot in IR - we get called about a bleed, review the imaging, take the patient to angiography, and then get paged by diagnostic radiology in the middle of the case. Orthopedists have already wised-up to the disconnect between radiology reimbursement and the discrepancy in work involved in MR interpretation versus surgery. At least two groups, including the "best orthopedic hospital in the country" employ their own in-house radiologists so that they can capture part of the imaging revenue. If GPT-4 can offer summative reads without feature simplification, and prior to intervention, why not have the IR or orthopedist sign off the GPT-4 report?
soheil 4y ago
I also wonder how many AI startups doing medical vision will now go under.
- geraneum 4y ago
  None, unless “Open”AI really opens up about how and if their LLM can actually interpret the images like in their marketing material. We’re talking about medicine and a ton of regulations.

dpflan 4y ago

What is your take then on how this affect your field? And your occupation? Do you think you will incorporate such technology into your day-to-day?

aabajian 4y ago
I think it will be radiologists signing-off auto-generated reports, with less reimbursement per study. It'll likely result in more work for diagnostic radiologists to maintain their same salary levels.
- haldujai 4y ago
  It will take a very long time for this to happen, probably decades. Cardiologists are still paid to finalize ECG reports 3 days after a STEMI. I've worked at places with AI/CAD for lung nodules, mammo and stroke and there isn't even a whisper at cutting fee codes because of AI efficiency gains at the moment. N.B. I say this as a radiologist who elected not to pursue an interventional fellowship because I see reimbursement for diagnostic work skyrocketing with AI due to increases in efficiency and stagnant fee codes.
- reubens 4y ago
  It’s hard to imagine this not happening in the next five years. Just depends on who is prepared to take on the radiologists to reduce their fee codes. Speaking as 2nd year radiology resident in Australia

sinuhe69 4y ago

It all comes down to labelled data. There are millions images of VGA connectors and lightning cables on the internet with description, where CLIP model and similar could learn to recognize them relatively reliably. On the other hand, I'm not sure such amount of data are available for AI training. Especially if the diagnostic is blinded, it will be even harder for the AI model to reliably differentiate between them, making cross-disease diagnostic hard. Not to mention the risk and reliability of such tasks.

bick_nyers 4y ago
As someone who has worked at a Radiology PACS with petabytes of medical images under management, this is 100% accurate. You might have images, but not the diagnoses to train the AI with. In addition, there are compliance reasons, just because you manage that data doesn't mean that you can train an AI on it and sell it, unless of course you get explicit permission from every individual patient (good luck). I do believe that with enough effort we could create AI specialist doctors, and allow the generalist family doctor to make a comeback, augmented with the ability to tap into specialist knowledge. Technology in the medical industry is extremely far behind modern progress though, CT images are still largely 512 by 512 pixels. It's too easy to get bogged down with legacy support to make significant advancements and stay on the cutting edge.
- in3d 4y ago
  Seems like this is where centralized countries like China can get a significant edge over the U.S.
- gwern 4y ago
  > You might have images, but not the diagnoses to train the AI with. That's what the unsupervised learning is for. GPT doesn't have labels either, just raw data.
  - rjtavares 4y ago
    There isn't an obvious unsupervised problem to train medical imaging with. What's the medical imaging equivalent to "predict the next word"?

hospitalJail 4y ago

If you are in the US. It is more important to have the legal paperwork, than to be factually correct. The medical cartels always will get their cut.

ChickenNugger 4y ago
I'm curious who the medical cartels in this context. Can you elaborate?
- hospitalJail 4y ago
  See for yourself https://www.opensecrets.org/federal-lobbying/top-spenders?cycle=a https://www.opensecrets.org/federal-lobbying/top-spenders?cy...
barking_biscuit 4y ago
username checks out
- pmoriarty 4y ago
  Please don't do this on HN.
  - barking_biscuit 4y ago
    Okay
- Hoyadonis 4y ago
  [flagged]
  - barking_biscuit 4y ago
    Okay
bpodgursky 4y ago
Eventually it's going to be cheap enough to drop by Tijuana for $5 MRI that even the cartel has to react. Also, even within the US framework, there's pressure. A radiologist can rubberstamp 10x as many reports with AI-assistance. That doesn't eliminate radiology, but it eliminates 90% of the radiologists we're training.

hot_gril 4y ago

In 2015, I took an intro cognitive science class in college. The professor listed some natural language feats that he was certain AI would never accomplish. It wasn't long before average people were using AI for things he predicted were impossible.

haldujai 4y ago

As another radiologist, I'm not sure how you can say this with a straight face? If anything the minimal progress that has been made since Hinton made this claim should be encouraging people to pursue radiology training. As with other areas of medicine that have better AI (interpreting ECGs for example) all this will do is make our lives easier. AI is not an existential threat to radiology (or pathology for that matter which is an easier problem to solve than medical imaging). 1. Radiology =/= interpreting pixels and applying a class label. 2. Risk and consequences of misclassifying T-staging of a cancer =/= risk of misclassifying a VGA connector. 3. Imaging appearance overlap of radiological findings >>>>>>>>>> imaging appearance overlap of different types of connectors (e.g. infection and cancer can look the same, we make educated guesses on a lot of things considering many patient variables, clinical data, and prior imaging.) You would need to have a multi-modal model enriched with a patient knowledge graph to try and replicate this, while problems like this are being worked on we are no where close enough for this to be a near-term threat. We haven't even solved NLP in medicine, let alone imaging interpretation! 4. Radiologists do far more than interpret images, unless you're in a tele-radiology eat-what-you-kill sweatshop. This includes things like procedures (i.e. biopsies and drainages for diagnostic rads) and multidisciplinary rounds/tumor boards.

aabajian 4y ago
I agree that the level of risk/consequence is higher for radiology misses, but I wonder if radiologists are already missing things because of simplification for human feasibility. Things like LI-RADS and BI-RADS are so simple from a computer science perspective. I wouldn't even call them algorithms, just simple checkbox decision making. This tendency to simplify is everywhere in radiology: When looking for a radial head fracture, we're taught to exam the cortex for discontinuities, look for an elbow joint effusion, evaluate the anterior humeral line, etc. But what if there's some feature (or combination of feature) that is beyond human perception? Maybe the radial ulnar joint space is a millimeter wider than it should be? Maybe soft tissues are just a bit too dense near the elbow? Just how far does the fat pad have to be displaced to indicate an effusion? Probably the best "decision function" is a non-linear combination of all these findings. Oh, but we only have 1 minute to read the radiograph and move on to the next one. Unfortunately, as someone noted below, advances in medicine are glacially slow. I think change is only going to come in the form of lawsuits. Imagine a future where a patient and her lawyer can get a second-opinion from an online model, "Why did you miss my client's proximal scaphoid fracture? We uploaded her radiographs and GPT-4 found it in 2 seconds." If and when these types of lawsuits occur, malpractice insurances are going to push for radiologists to use AI. Regarding other tasks performed by radiologists, some radiologists do more than dictate images, but those are generally the minority. The vast majority of radiologists read images for big money without ever meeting the patient or the provider who ordered the study. In the most extreme case, radiologists read studies after the acute intervention has been performed. This happens a lot in IR - we get called about a bleed, review the imaging, take the patient to angiography, and then get paged by diagnostic radiology in the middle of the case. Orthopedists have already wised-up to the disconnect between radiology reimbursement and the discrepancy in work involved in MR interpretation versus surgery. At least two groups, including the "best orthopedic hospital in the country" employ their own in-house radiologists so that they can capture part of the imaging revenue. If GPT-4 can offer summative reads without feature simplification, and prior to intervention, why not have the IR or orthopedist sign off the GPT-4 report?

soheil 4y ago

I also wonder how many AI startups doing medical vision will now go under.

geraneum 4y ago
None, unless “Open”AI really opens up about how and if their LLM can actually interpret the images like in their marketing material. We’re talking about medicine and a ton of regulations.