12 ms·

The A.I. Radiologist Will Not Be with You Soon

119 points by voxadam 1y ago

deleted 1y ago
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voxadam 1y ago
https://archive.is/e2phZ https://archive.is/e2phZ
seesthruya 1y ago
Working radiologist here, 20 years experience. This article is surprisingly accurate. I fully expect to finish my career without being 'replaced' by AI. Happy to debate/answer questions :-)
- janice1999 1y ago
  Are AI models able to detect abnormalities that even an experienced radiologist can't see? i.e. something that would look normal to a human eye but AI correctly flags it for investigation? Or are all AI detections 'obvious' to human eyes and simply a confirmation? I suspect the latter since it was human annotated images the model was trained on.
  - seesthruya 1y ago
    Depends on what you mean by 'see'. For example, let's say I'm looking at a chest x-ray. There is a pneumonia at the left lung base and I am clever enough to notice it. 'Aha', I think, congratulating myself at making the diagnosis and figuring out why the patient is short of breath. But, in this example, I stop looking closely at the X-ray after noticing the pneumonia, so I miss a pneumothorax at the right lung apex. I have made a mistake radiologists call 'satisfaction of search'. My 'search' for the patient's problem was 'satisfied' by finding the pneumonia, and because I am human and therefore fundamentally flawed, I stopped looking for a second clinically relevant diagnosis. An AI module that detects a pneumothorax is not prone to this type of error. So it sees something I did not. But it doesn't see something that I can't see. I just didn't look.
    - ceejayoz 1y ago
      This is definitely a thing. https://www.npr.org/sections/health-shots/2013/02/11/171409656/why-even-radiologists-can-miss-a-gorilla-hiding-in-plain-sight https://www.npr.org/sections/health-shots/2013/02/11/1714096... I'm skeptical to the claim that AI isn't prone to this sort of error, though. AI loves the easy answer.
      - the_sleaze_ 1y ago
        AI is overloaded. An LLM loves the easy answer, but that's not what is underlying an image classification model.
    - alabastervlog 1y ago
      > I have made a mistake radiologists call 'satisfaction of search'. Ah, now I have a name for it. When I've chased a bug and fixed a problem I found that would cause the observed problem behavior, but haven't yet proven the behavior is corrected, I'm always careful to specify that "I fixed a problem, but I don't know if I fixed the problem". Seems similar: found and fixed a bug that could explain the issue, but that doesn't mean there's not another one that, independently, would also cause the same observed problem.
      - ceejayoz 1y ago
        It's also called inattentional blindness. https://en.wikipedia.org/wiki/Inattentional_blindness https://en.wikipedia.org/wiki/Inattentional_blindness
  - bilbo0s 1y ago
    I've been going to RSNA for over 25 years, in all that time, the best I've seen from any model presented to me was the smack the radiologist on the head and say, "you dummy, you should have seen that!" model. That is, the models spot pathologies that 99.9999% of rads would spot anyway if not overworked, tired, or in a hurry. But, addressing the implication of your question, the value is actually in spotting a pathology that 99.9999% of rads would never spot. In all my years developing medical imaging startups and software, I've never seen it happen. I don't expect to see it in my lifetime.
    - seesthruya 1y ago
      I have a fairly strong background in tech, and I've been programming computers since 1979 when my dad bought me a TRS-80. Tape drives FTW! I agree with almost everything you've said here. Except 'not in my lifetime', because I plan on living for a very long time, and who knows what those computer nerds will come up with eventually ;-)
    - SketchySeaBeast 1y ago
      I'm sure it's a matter of training data, but I don't know if it's a surmountable problem. How do you get enough training data for the machine to learn and reliably catch those exceptions?
    - nearbuy 1y ago
      One Harvard study trained an AI that could reliably determine someone's race from a chest X-ray. AIs can be trained to see things we can't. The difficulty is likely in making a good training dataset of labeled images with pathologies radiologists couldn't see. I imagine in some cases (like cancer), you may happen to have an earlier CT scan or X-ray from the patient where the pathology is not quite yet detectable to the human eye.
      - devilbunny 1y ago
        I suspect that radiologists could identify race from a plain chest x-ray if they were given the patient’s race and asked to start noticing the difference. They just aren’t doing it because, if it’s important, you can just look at the patient. There are a lot of things in medicine that aren’t in literature, but are well-known among certain practitioners. I’m an anesthesiologist and practice in an area with a large African-American population. About 10-15% (rough guess) of people of West African descent will have a ridiculously strong salivary response to certain drugs (muscarinic agonists). As in, after one dose their mouths will be full of saliva in seconds. We don’t have East Africans for comparison, so I can’t say it’s a pan-Bantu thing, but I have seen it in a Nigerian who lived here. Not in the literature, but we all know it. I had a (EDIT: non-anesthesia) colleague ask me about a hypersecretory response from such a drug. I said, oh, was he black? Yes, how did I know? Because we give those drugs all the time and have eyes. It’s very rare to see in European-descended populations.
- bilbo0s 1y ago
  Medical Imaging tech entrepreneur here. Been going to RSNA for longer than you've been a radiologist. In all that time, I've never come across an AI that I felt was fit for purpose. I wholeheartedly agree with you. Many many reasons for this, and I'm happy to chime in from the tech side of things and fill in any blanks outside your knowledge domain.
  - deleted 1y ago
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  - seesthruya 1y ago
    Basically, they are all currently hot garbage. In my practice we have: 1. Mammography CAD 2. Pulmonary nodule CAD 3. Intracranial hemorrhage CAD 4. Intracranial vessel CAD They all stink! I would love it if they were useful, radiology is insanely difficult and I could use the help!
  - lostlogin 1y ago
    > Medical Imaging tech entrepreneur here I initially read this as ‘Medical Imaging tech who is an entrepreneur.’ I now think you’re a radiologist? Any particular interests? Fixing the shiite RIS/PACS world and the hell of hl7 would make me happy. I’m an MR tech, and just finished trying to make a scan description ‘MRI Cervical Spine + Right Brachial Plexis’ You can’t. Description too long.
- M95D 1y ago
  Respectfully, it doesn't matter what you expect or think. What matters is this: - If the law allows AI to replace you. - If the hospital/company thinks [AI cost + AI-caused law suits] will be less expensive that [your salary + law suites caused by you]. I'm almost in the same situation as you are. I have 22 years left until retirement and I'm thinking I should change my career before I'm too old to do it.
  - candiddevmike 1y ago
    What career would you change to that would be safe, given the conditions you provided and your time horizon?
    - deleted 1y ago
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    - jerf 1y ago
      The original author of the paper about the technological singularity [1] defines it as simply the point where predictions break down. If AI gets to the point where it is truly replacing radiologists and programmers wholesale, it is difficult to tell anyone what to do about it today, because that's essentially on the other side of the singularity from here. Who knows what the answer will be? (Ironically, the author of that paper, being also a science fiction author, is also responsible for morphing the singularity into "the rapture for nerds" in his own sci-fi writing. But I find the original paper's definition to have more utility in the current world.) [1]: https://accelerating.org/articles/comingtechsingularity https://accelerating.org/articles/comingtechsingularity
      - LPisGood 1y ago
        I think that if AI can replace software engineers then AI can replace any job because the domain of software engineering is pretty much everything.
        FeteCommuniste 1y ago
- HelloMcFly 1y ago
  As my wife says: "Until it's as easy to sue AI as it is doctors, we probably won't see AI replacing doctors."
  - candiddevmike 1y ago
    AI in healthcare is going to add so many layers of indirection for malpractice lawsuits. You'll spend years and lots of $$$ trying to figure out who the defendant would ultimately be, only for it to end up being a LLC that unfortunately just filed for bankruptcy.
  - ogogmad 1y ago
    What if ppl just bought the equipment and did the scans at home?
    - ceejayoz 1y ago
      Have you priced out a CT scanner and MRI? Will you be able to source a radioactive source for your x-rays?
      - ogogmad 1y ago
        Fair. But what if ppl instead got scans in "radiology shops" without waiting for a specialist? Specialists are expensive.
        SketchySeaBeast 1y ago
        Isn't that 90% of going to get scan is right now? You'll still need the "shop" to provide the equipment and the tech with the training to know what/where to scan, but you might get the results a bit faster? Are the radiologists the chokepoint now, or is it the techs?
        eitally 1y ago
        That's the way it already works in many cases, just like with outpatient surgery clinics and other outpatient specialist practices. There is a critical difference, though, because radiology also has sub-specialities and someone focused on orthopedics probably isn't the one you'd want reading your cardiology images, nor would you want your ophthalmologic radiologist trying to diagnose a brain CT.
- stackedinserter 1y ago
  What's accurate in this article? It's very vague, it can be tldred into "we won't go anywhere, although AI does more and more of our work" > Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience. AI will do that more efficiently, and probably already does. "tapping years of experience" is just data in training set. > A.I. can also automatically identify images showing the highest probability of an abnormal growth, essentially telling the radiologist, “Look here first.” Another program scans images for blood clots in the heart or lungs, even when the medical focus may be elsewhere. > “A.I. is everywhere in our workflow now,” Dr. Baffour said. > “Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.” Maybe you'll be able to happily retire because inertia, but overall it looks like elevator operator job. What's so special about radiology?
  - seesthruya 1y ago
    There's nothing special about radiology. And I do believe inertia will carry me through the end of my career, which has approximately 10 years left. However, it's my opinion that my job takes general intelligence, not just pattern matching. Therefore, when I lose my job to AI, so does everyone else.
    - doctorpangloss 1y ago
      On the one hand, you’re totally right. The job takes general intelligence. On the other hand, a lot of jobs take general intelligence. You’re right about that too. It’s difficult to guess the specifics of your life, but: maybe you’ve engaged a real estate agent. Some people use no real estate agent. Some have a robo agent. No AI involved. Maybe you have written a will. Some people go online and spend $500 on templates from Trust & Will, others spend $3,000 on a lawyer to fill in the templates for them, some don’t do any of that at all. Even in medicine, you know, a pharma rep has to go and convince someone to add their thing to the guidelines, and you can look back at the time between the study and adoption as, well people were intelligent and there was demand, but doctors were not doing so and so thing due to lack of essentially sales. I mean you don’t have to be generally intelligent to know that flossing is good for you, and yet: so few people floss! That would maybe not put tons of dentists out of business. But people are routinely doing (or not doing) professional services stuff not for any good (or bad) reason at all. Clearly the thing going on in the professional services economy isn’t about general intelligence - there’s already lots of stuff that is NOT happening long before AI changes the game. It’s all cultural. If you’ve gotten this far without knowing what I am talking about… listen, who knows what’s going to happen? Clearly a lot of behavior is not for any good reason. How do you know where the ball is going to go for culture? Personally I think it’s a kind of arrogant position: “I’m a member of the guild, and from my POV, if my profession is replaced, so is everyone else’s.” Arrogance is not an attractive culture, it’s an adversarial one! And you could say inertia, and yet: look who’s running the HHS! There are kids right now, that I know in my real life, who look like you or me, who went to fancy Ivy League school, and they are vaccine skeptical. What about inertia and general intelligence then? So I’ll just say, you know, putting yourself out here on this forum, being all like, “I will AMA, I am the voice,” and then to be so arrogant: you are your own evidence for why maybe it won’t last 10 years.
- Al-Khwarizmi 1y ago
  If (as acknowledged in the article) AI automates at least part of the work of radiologists (e.g. tool that "saves her 15 to 30 minutes each time she examines a kidney image"), don't you fear that the demand of radiologists will decline? Even if some are still needed, surely if a hospital needs X reports per day and now Y radiologists are sufficient to provide them rather than the current Z (Y<Z), that should be something for people considering your career to take into account? On the other hand, how much of your confidence in not being replaced stems from AI not being able to do the work, and how much from legal/societal issues (a human needing to be legally responsible for the diagnoses)? Honestly the description in the article of what a radiologist does "Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience" doesn't strike me as anything impossible for AI within a few years, now that models are multimodal and they can work with increasing amounts of text (e.g. medical histories).
  - dingnuts 1y ago
    if the cost for preventative scans goes down, demand will rise. medical demand is incredibly constrained by price. people skip all kinds of tests they need because they can't afford it. the radiologists will have more work to do, not less.
  - seesthruya 1y ago
    There is a national shortage of radiologists in the US, with many hospital systems facing a backlog of unread cases measuring in the thousands. And, the baby boomers are starting to retire, it's only going to get worse. We aren't training enough new radiologists, which is a different discussion. Askl to your question on where my confidence stems from, there are both legal reasons and 'not being able to do the work' reasons. Legal is easy, the most powerful lobby in most states are trial attorneys. They simply won't allow a situation where liability cannot be attached to medical practice. Somebody is getting sued. As to what I do day to day, I don't think I'm just matching patterns. I believe what I do takes general intelligence. Therefore, when AI can do my job, it can do everyone else's job as well.
- deleted 1y ago
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- belly_joe 1y ago
  I would say generally speaking that people who assume AI will replace somebody else's job believe that these jobs are merely mechanical and there is no high-level reasoning involved that would basically require AGI (when that comes about nobody is safe). So the model of the AI radiologist assumes the only job of a radiologist is to classify images, which is pretty vulnerable to near-future disruption. I imagine, given the training involved, the job involves more than just looking at pictures? This is what I would like to see explained. The analogy would be the "95% of code is written by AI" stat that gets trotted out, replacing code with image evaluation. Yes AI will write the code but someone has to tell the AI what to write which is the tricky part.
  - seesthruya 1y ago
    100%
  - tbrownaw 1y ago
    We already have AI taxis (in specific limited areas, but still). Driving isn't something I'd usually call "merely mechanical".
    - tintor 1y ago
      Driving (in US) is considered unskilled labor.
    - cess11 1y ago
      That is such a contrived phenomenon, it has taken decades of lobbying and destruction of political accountability to create the conditions where a person considered sane would touch that idea instead of immediately skipping over to driverless trains. Incredibly wasteful gimmick, I don't get why the usians are still struggling away at it now that the chinese seem to have already done it.
      - SpicyLemonZest 1y ago
        I don't know where you've gotten the idea that cars are something you can "skip over" on the way to trains. Public transit is great, and the US should do it better, but it doesn't obsolete every other form of transit. The vast majority of people in every country who can afford access to cars use them regularly.
- tarunkotia 1y ago
  I worked on an autocontouring model but we could not get very high accuracy for it to be adopted commercially. The algorithm would work for some organs but would totally freak-out on the others. And if the anatomy was out of norm then it would not work at all. This was 5 years ago, I see Siemens [0] has a similar tool. I remember shadowing a dosimetrist contouring all the Organs-At-Risk (OAR) and it took about 3-4 hours to contour one CT image of thoracic region. Do you know how much better the autocontouring tools have become? [0] https://www.siemens-healthineers.com/en-us/radiotherapy/software-solutions/autocontouring https://www.siemens-healthineers.com/en-us/radiotherapy/soft...
  - justlikereddit 1y ago
    Segmenting algorithms is massively improved from 5 years ago. How much the actual SOTA is used on the work floor though is always a hit and miss.
    - seesthruya 1y ago
      Agreed, and from what I understand radiation oncology treatment planning has benefited from this.
- jjtheblunt 1y ago
  (great username: a radiologist with "seesthruya")
- LanceH 1y ago
  At the same time it can be a handy tool to be a first cut at triage. It's really not a matter of "full replacement or bust".
  - seesthruya 1y ago
    This is true!
- TuringNYC 1y ago
  >> Happy to debate/answer questions :-) Curious -- do you think that is because 1. the technology isnt there, or 2. because it isnt a competitive market (basically, the American Board of Radiologists controls standards of practice and can slow down technologies seen as competitive to human doctors)? 3. or perhaps 1 doesnt happen because outsiders know the market is guarded by the market itsself?
- bko 1y ago
  I don't think it will go away as long as we have third party paying for the costs and AMA controlling competition. If I had to pay $500 or whatever to get a scan, and instead I could get my data, send it to a model and only follow up if it came back bad, I would. But now someone else pays and there are laws and regulations that prevent people from controlling their data, or at least make it difficult. Kind of weird I have a file on me that I have never seen.
  - lostlogin 1y ago
    > Kind of weird I have a file on me that I have never seen. Can’t you ask for it?
  - nradov 1y ago
    Nope. You have an absolute legal right to obtain copies of your medical images and other data. Who paid for it is irrelevant. The provider can charge you a nominal fee for copying the files but they can't keep it from you. https://www.hhs.gov/hipaa/for-individuals/guidance-materials-for-consumers/index.html https://www.hhs.gov/hipaa/for-individuals/guidance-materials...
xnx 1y ago
Glad to hear that this is one area that AI is conclusively useful. Still not clear that the already superhuman capabilities of AI won't still fully supplement radiologist interpretive skills with every additional bit of training data that comes in.
pj_mukh 1y ago
"The staff has grown 55 percent since Dr. Hinton’s forecast of doom, to more than 400 radiologists." Wonder what other forecasts of doom he is wrong about :|.
- yread 1y ago
  They are also all driving themselves to the hospital instead of using self-driving cars. Different forecaster though
  - varelse 1y ago
    [dead]
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29athrowaway 1y ago
For an ML model, a sofa with a tiger pattern might be a tiger, if in its training dataset tiger stripes always means tiger. It does not have common sense.
- qgin 1y ago
  For small models, yes. But for the kind of massive multimodal models getting trained these days, the concept of "a pattern on an object" will exist. It doesn't need to have seen a tiger sofa before. But tiger + sofa + patterns on an object is enough to not run from the tiger print.
roenxi 1y ago
This reminds me of the idea that Human-Chess partnerships would be the ultimate manifestation of Chess genius. I'm not sure whether the idea is still holding on but engines are so far ahead of human play that I doubt a human in the loop can add anything these days given how devastatingly far ahead the engines are and the advent of machine learning techniques.
- husarcik 1y ago
  Chess reminds me more of programming given the set of defined rules in each. However, I'm biased as I work in radiology and program more as a hobby. So far I've seen way more tools to help me code than to accurately detect radiologic findings.
  - ogogmad 1y ago
    So far. Computer vision is currently lagging NLP, but I wouldn't expect that to last.
    - radlad 1y ago
      > but I wouldn't expect that to last Do you have any links to research or work being done on computer vision that leads you to this conclusion? Would love to check it out!
      - startupsfail 1y ago
        You can compare best image synthesis and image understanding from two decades ago (SIFT / HOG), from a decade ago (CNN, SdA) and now (Transformer). Very rapid progress that went from being able to unreliability recognize a face to getting to outperforming human professionals (see MMMU) is quite remarkable.
        radlad 1y ago
        AIUI, and I may be wrong, but each of the mentioned technologies was a "breakthrough" technology - not iterative improvement. Along this vein, I was wondering if there was some promising, novel research OP was aware of for image understanding. The most recent of which you mentioned, Transformers, is used by both LLMs and image synthesis/understanding. The parent posits that while computer vision lags behind NLP, this may not continue. While your comment points out that image synthesis and understanding has improved over time, I'm not sure I follow the argument that it may soon leapfrog or even catch up with LLMs (i.e. text understanding and synthesis.)
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bobowzki 1y ago
The thing I find most interesting about ML in radiology is that a computer can observe the entire dynamic range of the sensor at once. A human will only look at a window or a compressed window.
- husarcik 1y ago
  This is a very key point. Perhaps that discrepancy can be leveraged in image generation to save time.
  - bilbo0s 1y ago
    It already is, which is why rads input window/level settings.
- deleted 1y ago
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sulam 1y ago
“Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience.” Now think about how much of software development is typing out the code vs talking to people, getting a clear definition of the problem, debugging, etc. (I would love an LLM that could debug problems in production — but all they can do is tell me stuff I already know). Then layer on that there are far more ideas for what should be built than you have time to actually build in every organization I’ve ever worked in. I’m not worried about my job. I’m more worried my coworkers won’t realize what a great tool this is and my company will be left in the dust.
- deleted 1y ago
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- agos 1y ago
  that great tool does not need ten years of experience to use. your coworkers will be able to catch up quite easily
  - sulam 1y ago
    Fwiw I think this is not true right now. It might be true in the future, I guess. Today you have to learn some pretty specific techniques and they don’t come naturally to most engineers I think. They are accustomed to one-shot Google searches, not prompt engineering. Yes, you can learn how to prompt engineer, but it takes trial and error. A lot of my co-workers today still believe the LLM is useless because it hallucinates API definitions. They very much are not used to tools that require the level of hand-holding you need today. Either teaching them is going to get easier, which is somewhat plausible actually — or you think LLMs are going to get dramatically easier to prompt with more reliable output. I actually think that’s somewhat implausible. Log(compute) is a pretty hard number to move past a certain point and it seems we’ve hit that point already. RLHF is great, but it doesn’t scale well and the result is that some tools are hard to build. Giving me millions of context tokens is neat, but if it’s wildly expensive and confuses the model on top of that, it’s not actually useful. I do think there is room to improve and of course there are new techniques to be discovered. The models may even help discover some of them, although I am skeptical that they will find Everest by hill climbing when they’re currently on Pike’s Peak. Or maybe we really need to find Olympus Mons. Tl;dr — maybe!
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EcommerceFlow 1y ago
So are datasets/currently available data the limitation here? Let's say a major healthcare leak occurred, involving millions of images and associated doctor notes, diagnostics, etc... would this help advance the field or is it some algorithmic issue?
d_burfoot 1y ago
The key to the power of the LLM is that the training process can learn effectively from vast corpora of unlabelled text. Unfortunately, there is no comparably vast database of medical images. In order to "crack" radiology, the AI companies would need to launch an enormous data collection program involving thousands of hospitals across the world. Every time you got an MRI or X-Ray, you would sign some disclosure form that allowed your images to be anonymously submitted to the central data repository. This kind of project is very easy to describe, but very difficult to execute.
- physicsguy 1y ago
  They’ll have better luck in countries like the U.K. where medical data is at least somewhat more organised by virtue of being under the NHS umbrella
  - nradov 1y ago
    I don't think the UK NHS has a single centralized PACS or EHR.
    - physicsguy 1y ago
      It's organised into regional NHS primary care and acute trusts, which usually share their records these days. It's still substantially more integrated than in many other countries.
- seesthruya 1y ago
  I agree with you, but here is where things get tricky: Everyday I see something on a scan yhat I've never seen before. And, possibly, no one has ever seen before. There is tremendous variation in human anatomy and pathology. So what do I do? I use general intelligence. I talk to the patient. I talk to the referring doctor. I compare with other studies, across modalities and time. I reason. I synthesize. I think. So my point is, basically, radiology takes AGI.
  - ACCount36 1y ago
Workaccount2 1y ago
I just want to point out that the term "A.I." gets used pretty loosely in these articles, as if A.I. is a monolithic commodity that you plugin to your software to make it do chatGPT. The example in the article is an in house developed "A.I." to help radiologists assess images. Digging a bit deeper it seems they are using mostly old CNN type architectures with a few million parameters.[1] I think it still remains to be seen what a 1T+ parameter transformer trained specifically for radiology will do. I think anyone would be confident that a locally run CNN will not hold a candle to it. [1]https://mayo-radiology-informatics-lab.github.io/MIDeL/index.html https://mayo-radiology-informatics-lab.github.io/MIDeL/index...
- seesthruya 1y ago
  Agreed
- heyitsguay 1y ago
  There are many, many papers and projects out there about tuning foundation models on various types of medical imaging data, and many organized efforts to produce large medical imaging datasets to feed that training. This stuff is well-known in the trenches and can improve on the older, smaller CNNs in some ways, but not in a way that's produced any step change in automated capabilities. People are certainly working on it!
- bko 1y ago
  > I think it still remains to be seen what a 1T+ parameter transformer trained specifically for radiology will do Does image processing of something like this scale with parameters? It makes sense that language continues to scale as the vector space is huge. Even models that generate images scale as the problem space is so large. But here there are only so many pixels in the image and they are a lot more uniform. You likely can't have 1T images to train on, so wouldn't the model just overfit and basically memorize every image it has ever seen?
  - throwuxiytayq 1y ago
    If the meat machine can learn it, then the silicon machine can learn it.
airstrike 1y ago
So will it cost me less than $1.5k the next time I need an x-ray?
- agos 1y ago
  of course not, that would be silly. it will cost $1.5k + the AI analysis fee
qgin 1y ago
People get tripped up by thinking "there is a subset of what I do that only humans can do and so that means AI will not eliminate my profession entirely and my job is safe". Let's assume for now that it's true that AI can't do a certain subset of your work. Your profession won't be eliminated from the earth, that's true. But if 80% of your work can be done by AI, 80% of your work will be done by AI. There will still be humans kept around for that remaining 20%, but fewer of them will be needed.
- nradov 1y ago
  The demand for radiologists is effectively infinite. Right now the healthcare system is supply constrained. If AI tools reduce the work of reading an image then more imaging studies will be ordered. Also, many radiologists do interventional procedures directly with patients. We're a long, long way from being able to significantly automate that work.
  - seesthruya 1y ago
    This is 100% true and very insightful, but not obvious to people who don't work in healthcare.
  - SoftTalker 1y ago
    So I recently needed an MRI to evaluate a rotator cuff injury. Because MRIs are expensive, insurnace required that I do 6 weeks of physical therapy first to see if that would resolve it. Is this because the imaging process is so expensive, or because the radiologist who reads the image is so expensive? If demand for an expensive imaging process increases, it will get even more expensive.
    - nradov 1y ago
      MRI machines and radiologist reads are both expensive. These are among the largest areas of medical spending for most health plans (and those costs are largely passed on to subscribers and employers). Many rotator cuff injuries do improve to some extent with targeted physical therapy. You can accomplish a lot by strengthening the stabilizer muscles, and by learning how to set your shoulder joint in the correct position before taking a load. So the insurer's position is reasonable for most cases. If the injury is more severe then your doctor can submit a request to skip the normal step therapy process.
qwertox 1y ago
TL;DR: “Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.”
epistasis 1y ago
For a more technical dive into startups that have been chugging along with AI in pathology for ~decade, check out this: https://www.owlposting.com/p/what-happened-to-pathology-ai-companies https://www.owlposting.com/p/what-happened-to-pathology-ai-c... > One pathology AI founder told me that it wasn’t hospitals or diagnostic labs that showed the most promise. It was R&D groups within Big Pharma. Those scientists and executives wanted new tooling. They were often sitting on massive internal datasets, had real budget allocated to experimental tech, and — critically — had a clear ROI if your model helped shave months off a study or more precisely target the right patient cohort. Most importantly, pharma didn’t care as much about the regulatory headaches, as they weren’t using your model to diagnose patients
- abhishaike 1y ago
  <3
- yread 1y ago
  Really nice article, thanks for sharing. With digital pathology ai startups going bust left and right it's quite an accurate analysis. One thing it's missing in the "product" path is preanalytical differences - same tissue processed in different labs can produce wildly different pixels.
MrBuddyCasino 1y ago
Reminder that according to https://openai.com/index/healthbench/ https://openai.com/index/healthbench/, not only do current AI models provide far better recommendations than doctors, but even if doctors know the AI recommendations as a starting point, human revision does not bring any improvement. Honestly this doesn't surprise me, considering the quality of the average doctor.
pseudocomposer 1y ago
I formerly worked at https://corista.com https://corista.com, dealing specifically with integrations with AI pathology algorithm vendors like https://www.qritive.com https://www.qritive.com and https://visiopharm.com https://visiopharm.com. (Note: we dealt specifically with microscopy rather than radiology.) I really think we were doing things the “right way” before I left: providing AI analyses from various vendors as overlays for slides, and being able to pre-flag slides with obvious cases of cancer or other infection. (These analyses typically being custom algorithms provided by vendors through APIs we collaborate on, not LLM output.) With things like this simply built into an existing, established pathology platform, I’m pretty confident a team of 4-5 pathologists could do the work of 6-8, with better quality output, similar to how Copilot and other tooling speeds us up as developers. At $300-400k/yr/doctor, that’s considerable savings (and the opportunity to allocate more doctors in specialties that aren’t easily automated). However, for lots of reasons, it seems the market doesn’t necessarily agree with me on the value potential of this approach in this field (which can certainly be a self-fulfilling prophecy).
- ProllyInfamous 1y ago
  >the value potential of this approach in this field As a medical school dropout, I think the largest "negative" in value assessment would be the inability to shift malpractice liability over to a human practitioner (i.e. can you insure a company's faulty AI/LLM?) — yet today I just read a HN article on insurers offering a new product: insuring chat/LLM fuck-ups. As exciting as this all is, I'm still so glad I didn't become a radiologist!
  - seesthruya 1y ago
    For what it's worth, I almost dropped out of medical school in my second year to go back to a career in tech. It was a close thing. Still, I have enjoyed my time in radiology. It is insanely challenging to do well, and has been very rewarding.
    - ProllyInfamous 1y ago
      I think I could have been happy with the physician lifestyle, but I knew I wouldn't make good life decisions surviving/getting through residency. I am glad to have survived (e.g. career change). I've spent two decades as an electrician, and as the story goes: it is what it is... but also what you make of it. An incredible Good Fortune keeps me from having to work full-time, but as I've entered my forties I know I want to get in an air conditioned office (before too long). Perhaps I'll first move to a country where healthcare isn't tied to full-time employment. Best of luck to you in your Interpretations.
jmward01 1y ago
If there is one thing that NN can do amazingly well it is pattern recognition. The biggest blockers to adoption are the EHR providers, regulations and just inertia. It will happen, the only question is when and to a small degree, how.
- nradov 1y ago
  EHR vendors aren't the major blocker here. Most of them have pretty good APIs now (at least for reading USCDI data from patient charts). But the data quality as entered by users is often crap: when you're training it's going to be garbage in / garbage out unless you put enormous efforts into cleansing. And the raw images are often not in the EHR but rather in a separate PACS.
  - jmward01 1y ago
    From what I have seen trying to build out new features into this area, EHR vendors often act like monopolies blocking a lot of competition by controlling any data exchange. The vendor lock-in is strong and very harmful to improving process and, ultimately, harms patient care.
    - nradov 1y ago
      What specifically are they blocking? The most popular EHRs from vendors like Epic and Oracle have extensive interoperability APIs for multiple open industry standards. In general you can read all USCDI data pretty easily. Writing data is more limited, for understandable reasons. And if you want to distribute a SMART on FHIR app then you'll have to go through some sort of approval process. The strong vendor lock-in is unavoidable given how deeply an EHR gets embedded into any provider organization. No matter what regulators and standards bodies do, it will never be easy to switch.
kazinator 1y ago
AI is not easily going to "steal" any properly gatekept jobs that require a licensed practitioner. Machine learning technology can be used to search for artifacts in medical diagnostic images that humans might miss. The flagged situations still have to be interpreted by a radiologist.
joshdavham 1y ago
> “People should stop training radiologists now,” Geoffrey Hinton said, adding that it was “just completely obvious” that within five years A.I. would outperform humans in that field. I'm starting to notice a strong trend where non-domain experts will confidently assert that X field will be replaced by AI.
- godelski 1y ago
  Isn't this a common trend with CS? It seems like there's just constant over hype that drives booms and crashes, even to such a degree that if something was there it can't even be built. We have billionaires and hundred millionaires that made their money on VR, blockchain, and Web3. The wealth comes even when no product does and that seems like a big issue that's being exploited
- calf 1y ago
  Hinton is a domain expert in AI, case in point Scott Aaronson cites AI revolutionizing protein-folding research, putting many protein-folding academics out of jobs. I'd argue that AI experts and CS theorists are in a better position to speak about technological change than the domain experts who are trained/selected mainly to practice the status quo.
  - a_bonobo 1y ago
    > putting many protein-folding academics out of jobs. Has this actually happened? I'd wager it's more like in OP's article, AI as a helper that speeds up research, enabling bigger or different research questions. Reliable prediction not of protein shape, but of protein-protein or protein-mRNA interactions, for example.

119 points by voxadam 1y ago

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voxadam 1y ago

https://archive.is/e2phZ https://archive.is/e2phZ

seesthruya 1y ago

Working radiologist here, 20 years experience. This article is surprisingly accurate. I fully expect to finish my career without being 'replaced' by AI. Happy to debate/answer questions :-)

janice1999 1y ago
Are AI models able to detect abnormalities that even an experienced radiologist can't see? i.e. something that would look normal to a human eye but AI correctly flags it for investigation? Or are all AI detections 'obvious' to human eyes and simply a confirmation? I suspect the latter since it was human annotated images the model was trained on.
- seesthruya 1y ago
  Depends on what you mean by 'see'. For example, let's say I'm looking at a chest x-ray. There is a pneumonia at the left lung base and I am clever enough to notice it. 'Aha', I think, congratulating myself at making the diagnosis and figuring out why the patient is short of breath. But, in this example, I stop looking closely at the X-ray after noticing the pneumonia, so I miss a pneumothorax at the right lung apex. I have made a mistake radiologists call 'satisfaction of search'. My 'search' for the patient's problem was 'satisfied' by finding the pneumonia, and because I am human and therefore fundamentally flawed, I stopped looking for a second clinically relevant diagnosis. An AI module that detects a pneumothorax is not prone to this type of error. So it sees something I did not. But it doesn't see something that I can't see. I just didn't look.
  - ceejayoz 1y ago
    This is definitely a thing. https://www.npr.org/sections/health-shots/2013/02/11/171409656/why-even-radiologists-can-miss-a-gorilla-hiding-in-plain-sight https://www.npr.org/sections/health-shots/2013/02/11/1714096... I'm skeptical to the claim that AI isn't prone to this sort of error, though. AI loves the easy answer.
    - the_sleaze_ 1y ago
      AI is overloaded. An LLM loves the easy answer, but that's not what is underlying an image classification model.
  - alabastervlog 1y ago
    > I have made a mistake radiologists call 'satisfaction of search'. Ah, now I have a name for it. When I've chased a bug and fixed a problem I found that would cause the observed problem behavior, but haven't yet proven the behavior is corrected, I'm always careful to specify that "I fixed a problem, but I don't know if I fixed the problem". Seems similar: found and fixed a bug that could explain the issue, but that doesn't mean there's not another one that, independently, would also cause the same observed problem.
    - ceejayoz 1y ago
      It's also called inattentional blindness. https://en.wikipedia.org/wiki/Inattentional_blindness https://en.wikipedia.org/wiki/Inattentional_blindness
- bilbo0s 1y ago
  I've been going to RSNA for over 25 years, in all that time, the best I've seen from any model presented to me was the smack the radiologist on the head and say, "you dummy, you should have seen that!" model. That is, the models spot pathologies that 99.9999% of rads would spot anyway if not overworked, tired, or in a hurry. But, addressing the implication of your question, the value is actually in spotting a pathology that 99.9999% of rads would never spot. In all my years developing medical imaging startups and software, I've never seen it happen. I don't expect to see it in my lifetime.
  - seesthruya 1y ago
    I have a fairly strong background in tech, and I've been programming computers since 1979 when my dad bought me a TRS-80. Tape drives FTW! I agree with almost everything you've said here. Except 'not in my lifetime', because I plan on living for a very long time, and who knows what those computer nerds will come up with eventually ;-)
  - SketchySeaBeast 1y ago
    I'm sure it's a matter of training data, but I don't know if it's a surmountable problem. How do you get enough training data for the machine to learn and reliably catch those exceptions?
  - nearbuy 1y ago
    One Harvard study trained an AI that could reliably determine someone's race from a chest X-ray. AIs can be trained to see things we can't. The difficulty is likely in making a good training dataset of labeled images with pathologies radiologists couldn't see. I imagine in some cases (like cancer), you may happen to have an earlier CT scan or X-ray from the patient where the pathology is not quite yet detectable to the human eye.
    - devilbunny 1y ago
      I suspect that radiologists could identify race from a plain chest x-ray if they were given the patient’s race and asked to start noticing the difference. They just aren’t doing it because, if it’s important, you can just look at the patient. There are a lot of things in medicine that aren’t in literature, but are well-known among certain practitioners. I’m an anesthesiologist and practice in an area with a large African-American population. About 10-15% (rough guess) of people of West African descent will have a ridiculously strong salivary response to certain drugs (muscarinic agonists). As in, after one dose their mouths will be full of saliva in seconds. We don’t have East Africans for comparison, so I can’t say it’s a pan-Bantu thing, but I have seen it in a Nigerian who lived here. Not in the literature, but we all know it. I had a (EDIT: non-anesthesia) colleague ask me about a hypersecretory response from such a drug. I said, oh, was he black? Yes, how did I know? Because we give those drugs all the time and have eyes. It’s very rare to see in European-descended populations.
bilbo0s 1y ago
Medical Imaging tech entrepreneur here. Been going to RSNA for longer than you've been a radiologist. In all that time, I've never come across an AI that I felt was fit for purpose. I wholeheartedly agree with you. Many many reasons for this, and I'm happy to chime in from the tech side of things and fill in any blanks outside your knowledge domain.
- deleted 1y ago
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- seesthruya 1y ago
  Basically, they are all currently hot garbage. In my practice we have: 1. Mammography CAD 2. Pulmonary nodule CAD 3. Intracranial hemorrhage CAD 4. Intracranial vessel CAD They all stink! I would love it if they were useful, radiology is insanely difficult and I could use the help!
- lostlogin 1y ago
  > Medical Imaging tech entrepreneur here I initially read this as ‘Medical Imaging tech who is an entrepreneur.’ I now think you’re a radiologist? Any particular interests? Fixing the shiite RIS/PACS world and the hell of hl7 would make me happy. I’m an MR tech, and just finished trying to make a scan description ‘MRI Cervical Spine + Right Brachial Plexis’ You can’t. Description too long.
M95D 1y ago
Respectfully, it doesn't matter what you expect or think. What matters is this: - If the law allows AI to replace you. - If the hospital/company thinks [AI cost + AI-caused law suits] will be less expensive that [your salary + law suites caused by you]. I'm almost in the same situation as you are. I have 22 years left until retirement and I'm thinking I should change my career before I'm too old to do it.
- candiddevmike 1y ago
  What career would you change to that would be safe, given the conditions you provided and your time horizon?
  - deleted 1y ago
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  - jerf 1y ago
    The original author of the paper about the technological singularity [1] defines it as simply the point where predictions break down. If AI gets to the point where it is truly replacing radiologists and programmers wholesale, it is difficult to tell anyone what to do about it today, because that's essentially on the other side of the singularity from here. Who knows what the answer will be? (Ironically, the author of that paper, being also a science fiction author, is also responsible for morphing the singularity into "the rapture for nerds" in his own sci-fi writing. But I find the original paper's definition to have more utility in the current world.) [1]: https://accelerating.org/articles/comingtechsingularity https://accelerating.org/articles/comingtechsingularity
    - LPisGood 1y ago
      I think that if AI can replace software engineers then AI can replace any job because the domain of software engineering is pretty much everything.
      - FeteCommuniste 1y ago
HelloMcFly 1y ago
As my wife says: "Until it's as easy to sue AI as it is doctors, we probably won't see AI replacing doctors."
- candiddevmike 1y ago
  AI in healthcare is going to add so many layers of indirection for malpractice lawsuits. You'll spend years and lots of $$$ trying to figure out who the defendant would ultimately be, only for it to end up being a LLC that unfortunately just filed for bankruptcy.
- ogogmad 1y ago
  What if ppl just bought the equipment and did the scans at home?
  - ceejayoz 1y ago
    Have you priced out a CT scanner and MRI? Will you be able to source a radioactive source for your x-rays?
    - ogogmad 1y ago
      Fair. But what if ppl instead got scans in "radiology shops" without waiting for a specialist? Specialists are expensive.
      - SketchySeaBeast 1y ago
        Isn't that 90% of going to get scan is right now? You'll still need the "shop" to provide the equipment and the tech with the training to know what/where to scan, but you might get the results a bit faster? Are the radiologists the chokepoint now, or is it the techs?
      - eitally 1y ago
        That's the way it already works in many cases, just like with outpatient surgery clinics and other outpatient specialist practices. There is a critical difference, though, because radiology also has sub-specialities and someone focused on orthopedics probably isn't the one you'd want reading your cardiology images, nor would you want your ophthalmologic radiologist trying to diagnose a brain CT.
stackedinserter 1y ago
What's accurate in this article? It's very vague, it can be tldred into "we won't go anywhere, although AI does more and more of our work" > Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience. AI will do that more efficiently, and probably already does. "tapping years of experience" is just data in training set. > A.I. can also automatically identify images showing the highest probability of an abnormal growth, essentially telling the radiologist, “Look here first.” Another program scans images for blood clots in the heart or lungs, even when the medical focus may be elsewhere. > “A.I. is everywhere in our workflow now,” Dr. Baffour said. > “Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.” Maybe you'll be able to happily retire because inertia, but overall it looks like elevator operator job. What's so special about radiology?
- seesthruya 1y ago
  There's nothing special about radiology. And I do believe inertia will carry me through the end of my career, which has approximately 10 years left. However, it's my opinion that my job takes general intelligence, not just pattern matching. Therefore, when I lose my job to AI, so does everyone else.
  - doctorpangloss 1y ago
    On the one hand, you’re totally right. The job takes general intelligence. On the other hand, a lot of jobs take general intelligence. You’re right about that too. It’s difficult to guess the specifics of your life, but: maybe you’ve engaged a real estate agent. Some people use no real estate agent. Some have a robo agent. No AI involved. Maybe you have written a will. Some people go online and spend $500 on templates from Trust & Will, others spend $3,000 on a lawyer to fill in the templates for them, some don’t do any of that at all. Even in medicine, you know, a pharma rep has to go and convince someone to add their thing to the guidelines, and you can look back at the time between the study and adoption as, well people were intelligent and there was demand, but doctors were not doing so and so thing due to lack of essentially sales. I mean you don’t have to be generally intelligent to know that flossing is good for you, and yet: so few people floss! That would maybe not put tons of dentists out of business. But people are routinely doing (or not doing) professional services stuff not for any good (or bad) reason at all. Clearly the thing going on in the professional services economy isn’t about general intelligence - there’s already lots of stuff that is NOT happening long before AI changes the game. It’s all cultural. If you’ve gotten this far without knowing what I am talking about… listen, who knows what’s going to happen? Clearly a lot of behavior is not for any good reason. How do you know where the ball is going to go for culture? Personally I think it’s a kind of arrogant position: “I’m a member of the guild, and from my POV, if my profession is replaced, so is everyone else’s.” Arrogance is not an attractive culture, it’s an adversarial one! And you could say inertia, and yet: look who’s running the HHS! There are kids right now, that I know in my real life, who look like you or me, who went to fancy Ivy League school, and they are vaccine skeptical. What about inertia and general intelligence then? So I’ll just say, you know, putting yourself out here on this forum, being all like, “I will AMA, I am the voice,” and then to be so arrogant: you are your own evidence for why maybe it won’t last 10 years.
Al-Khwarizmi 1y ago
If (as acknowledged in the article) AI automates at least part of the work of radiologists (e.g. tool that "saves her 15 to 30 minutes each time she examines a kidney image"), don't you fear that the demand of radiologists will decline? Even if some are still needed, surely if a hospital needs X reports per day and now Y radiologists are sufficient to provide them rather than the current Z (Y<Z), that should be something for people considering your career to take into account? On the other hand, how much of your confidence in not being replaced stems from AI not being able to do the work, and how much from legal/societal issues (a human needing to be legally responsible for the diagnoses)? Honestly the description in the article of what a radiologist does "Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience" doesn't strike me as anything impossible for AI within a few years, now that models are multimodal and they can work with increasing amounts of text (e.g. medical histories).
- dingnuts 1y ago
  if the cost for preventative scans goes down, demand will rise. medical demand is incredibly constrained by price. people skip all kinds of tests they need because they can't afford it. the radiologists will have more work to do, not less.
- seesthruya 1y ago
  There is a national shortage of radiologists in the US, with many hospital systems facing a backlog of unread cases measuring in the thousands. And, the baby boomers are starting to retire, it's only going to get worse. We aren't training enough new radiologists, which is a different discussion. Askl to your question on where my confidence stems from, there are both legal reasons and 'not being able to do the work' reasons. Legal is easy, the most powerful lobby in most states are trial attorneys. They simply won't allow a situation where liability cannot be attached to medical practice. Somebody is getting sued. As to what I do day to day, I don't think I'm just matching patterns. I believe what I do takes general intelligence. Therefore, when AI can do my job, it can do everyone else's job as well.
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belly_joe 1y ago
I would say generally speaking that people who assume AI will replace somebody else's job believe that these jobs are merely mechanical and there is no high-level reasoning involved that would basically require AGI (when that comes about nobody is safe). So the model of the AI radiologist assumes the only job of a radiologist is to classify images, which is pretty vulnerable to near-future disruption. I imagine, given the training involved, the job involves more than just looking at pictures? This is what I would like to see explained. The analogy would be the "95% of code is written by AI" stat that gets trotted out, replacing code with image evaluation. Yes AI will write the code but someone has to tell the AI what to write which is the tricky part.
- seesthruya 1y ago
  100%
- tbrownaw 1y ago
  We already have AI taxis (in specific limited areas, but still). Driving isn't something I'd usually call "merely mechanical".
  - tintor 1y ago
    Driving (in US) is considered unskilled labor.
  - cess11 1y ago
    That is such a contrived phenomenon, it has taken decades of lobbying and destruction of political accountability to create the conditions where a person considered sane would touch that idea instead of immediately skipping over to driverless trains. Incredibly wasteful gimmick, I don't get why the usians are still struggling away at it now that the chinese seem to have already done it.
    - SpicyLemonZest 1y ago
      I don't know where you've gotten the idea that cars are something you can "skip over" on the way to trains. Public transit is great, and the US should do it better, but it doesn't obsolete every other form of transit. The vast majority of people in every country who can afford access to cars use them regularly.
tarunkotia 1y ago
I worked on an autocontouring model but we could not get very high accuracy for it to be adopted commercially. The algorithm would work for some organs but would totally freak-out on the others. And if the anatomy was out of norm then it would not work at all. This was 5 years ago, I see Siemens [0] has a similar tool. I remember shadowing a dosimetrist contouring all the Organs-At-Risk (OAR) and it took about 3-4 hours to contour one CT image of thoracic region. Do you know how much better the autocontouring tools have become? [0] https://www.siemens-healthineers.com/en-us/radiotherapy/software-solutions/autocontouring https://www.siemens-healthineers.com/en-us/radiotherapy/soft...
- justlikereddit 1y ago
  Segmenting algorithms is massively improved from 5 years ago. How much the actual SOTA is used on the work floor though is always a hit and miss.
  - seesthruya 1y ago
    Agreed, and from what I understand radiation oncology treatment planning has benefited from this.
jjtheblunt 1y ago
(great username: a radiologist with "seesthruya")
LanceH 1y ago
At the same time it can be a handy tool to be a first cut at triage. It's really not a matter of "full replacement or bust".
- seesthruya 1y ago
  This is true!
TuringNYC 1y ago
>> Happy to debate/answer questions :-) Curious -- do you think that is because 1. the technology isnt there, or 2. because it isnt a competitive market (basically, the American Board of Radiologists controls standards of practice and can slow down technologies seen as competitive to human doctors)? 3. or perhaps 1 doesnt happen because outsiders know the market is guarded by the market itsself?
bko 1y ago
I don't think it will go away as long as we have third party paying for the costs and AMA controlling competition. If I had to pay $500 or whatever to get a scan, and instead I could get my data, send it to a model and only follow up if it came back bad, I would. But now someone else pays and there are laws and regulations that prevent people from controlling their data, or at least make it difficult. Kind of weird I have a file on me that I have never seen.
- lostlogin 1y ago
  > Kind of weird I have a file on me that I have never seen. Can’t you ask for it?
- nradov 1y ago
  Nope. You have an absolute legal right to obtain copies of your medical images and other data. Who paid for it is irrelevant. The provider can charge you a nominal fee for copying the files but they can't keep it from you. https://www.hhs.gov/hipaa/for-individuals/guidance-materials-for-consumers/index.html https://www.hhs.gov/hipaa/for-individuals/guidance-materials...

xnx 1y ago

Glad to hear that this is one area that AI is conclusively useful. Still not clear that the already superhuman capabilities of AI won't still fully supplement radiologist interpretive skills with every additional bit of training data that comes in.

pj_mukh 1y ago

"The staff has grown 55 percent since Dr. Hinton’s forecast of doom, to more than 400 radiologists." Wonder what other forecasts of doom he is wrong about :|.

yread 1y ago
They are also all driving themselves to the hospital instead of using self-driving cars. Different forecaster though
- varelse 1y ago
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29athrowaway 1y ago

For an ML model, a sofa with a tiger pattern might be a tiger, if in its training dataset tiger stripes always means tiger. It does not have common sense.

qgin 1y ago
For small models, yes. But for the kind of massive multimodal models getting trained these days, the concept of "a pattern on an object" will exist. It doesn't need to have seen a tiger sofa before. But tiger + sofa + patterns on an object is enough to not run from the tiger print.

roenxi 1y ago

This reminds me of the idea that Human-Chess partnerships would be the ultimate manifestation of Chess genius. I'm not sure whether the idea is still holding on but engines are so far ahead of human play that I doubt a human in the loop can add anything these days given how devastatingly far ahead the engines are and the advent of machine learning techniques.

husarcik 1y ago
Chess reminds me more of programming given the set of defined rules in each. However, I'm biased as I work in radiology and program more as a hobby. So far I've seen way more tools to help me code than to accurately detect radiologic findings.
- ogogmad 1y ago
  So far. Computer vision is currently lagging NLP, but I wouldn't expect that to last.
  - radlad 1y ago
    > but I wouldn't expect that to last Do you have any links to research or work being done on computer vision that leads you to this conclusion? Would love to check it out!
    - startupsfail 1y ago
      You can compare best image synthesis and image understanding from two decades ago (SIFT / HOG), from a decade ago (CNN, SdA) and now (Transformer). Very rapid progress that went from being able to unreliability recognize a face to getting to outperforming human professionals (see MMMU) is quite remarkable.
      - radlad 1y ago
        AIUI, and I may be wrong, but each of the mentioned technologies was a "breakthrough" technology - not iterative improvement. Along this vein, I was wondering if there was some promising, novel research OP was aware of for image understanding. The most recent of which you mentioned, Transformers, is used by both LLMs and image synthesis/understanding. The parent posits that while computer vision lags behind NLP, this may not continue. While your comment points out that image synthesis and understanding has improved over time, I'm not sure I follow the argument that it may soon leapfrog or even catch up with LLMs (i.e. text understanding and synthesis.)

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bobowzki 1y ago

The thing I find most interesting about ML in radiology is that a computer can observe the entire dynamic range of the sensor at once. A human will only look at a window or a compressed window.

husarcik 1y ago
This is a very key point. Perhaps that discrepancy can be leveraged in image generation to save time.
- bilbo0s 1y ago
  It already is, which is why rads input window/level settings.
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sulam 1y ago

“Radiologists do far more than study images. They advise other doctors and surgeons, talk to patients, write reports and analyze medical records. After identifying a suspect cluster of tissue in an organ, they interpret what it might mean for an individual patient with a particular medical history, tapping years of experience.” Now think about how much of software development is typing out the code vs talking to people, getting a clear definition of the problem, debugging, etc. (I would love an LLM that could debug problems in production — but all they can do is tell me stuff I already know). Then layer on that there are far more ideas for what should be built than you have time to actually build in every organization I’ve ever worked in. I’m not worried about my job. I’m more worried my coworkers won’t realize what a great tool this is and my company will be left in the dust.

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agos 1y ago
that great tool does not need ten years of experience to use. your coworkers will be able to catch up quite easily
- sulam 1y ago
  Fwiw I think this is not true right now. It might be true in the future, I guess. Today you have to learn some pretty specific techniques and they don’t come naturally to most engineers I think. They are accustomed to one-shot Google searches, not prompt engineering. Yes, you can learn how to prompt engineer, but it takes trial and error. A lot of my co-workers today still believe the LLM is useless because it hallucinates API definitions. They very much are not used to tools that require the level of hand-holding you need today. Either teaching them is going to get easier, which is somewhat plausible actually — or you think LLMs are going to get dramatically easier to prompt with more reliable output. I actually think that’s somewhat implausible. Log(compute) is a pretty hard number to move past a certain point and it seems we’ve hit that point already. RLHF is great, but it doesn’t scale well and the result is that some tools are hard to build. Giving me millions of context tokens is neat, but if it’s wildly expensive and confuses the model on top of that, it’s not actually useful. I do think there is room to improve and of course there are new techniques to be discovered. The models may even help discover some of them, although I am skeptical that they will find Everest by hill climbing when they’re currently on Pike’s Peak. Or maybe we really need to find Olympus Mons. Tl;dr — maybe!

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EcommerceFlow 1y ago

So are datasets/currently available data the limitation here? Let's say a major healthcare leak occurred, involving millions of images and associated doctor notes, diagnostics, etc... would this help advance the field or is it some algorithmic issue?

d_burfoot 1y ago

The key to the power of the LLM is that the training process can learn effectively from vast corpora of unlabelled text. Unfortunately, there is no comparably vast database of medical images. In order to "crack" radiology, the AI companies would need to launch an enormous data collection program involving thousands of hospitals across the world. Every time you got an MRI or X-Ray, you would sign some disclosure form that allowed your images to be anonymously submitted to the central data repository. This kind of project is very easy to describe, but very difficult to execute.

physicsguy 1y ago
They’ll have better luck in countries like the U.K. where medical data is at least somewhat more organised by virtue of being under the NHS umbrella
- nradov 1y ago
  I don't think the UK NHS has a single centralized PACS or EHR.
  - physicsguy 1y ago
    It's organised into regional NHS primary care and acute trusts, which usually share their records these days. It's still substantially more integrated than in many other countries.
seesthruya 1y ago
I agree with you, but here is where things get tricky: Everyday I see something on a scan yhat I've never seen before. And, possibly, no one has ever seen before. There is tremendous variation in human anatomy and pathology. So what do I do? I use general intelligence. I talk to the patient. I talk to the referring doctor. I compare with other studies, across modalities and time. I reason. I synthesize. I think. So my point is, basically, radiology takes AGI.
- ACCount36 1y ago

Workaccount2 1y ago

I just want to point out that the term "A.I." gets used pretty loosely in these articles, as if A.I. is a monolithic commodity that you plugin to your software to make it do chatGPT. The example in the article is an in house developed "A.I." to help radiologists assess images. Digging a bit deeper it seems they are using mostly old CNN type architectures with a few million parameters.[1] I think it still remains to be seen what a 1T+ parameter transformer trained specifically for radiology will do. I think anyone would be confident that a locally run CNN will not hold a candle to it. [1]https://mayo-radiology-informatics-lab.github.io/MIDeL/index.html https://mayo-radiology-informatics-lab.github.io/MIDeL/index...

seesthruya 1y ago
Agreed
heyitsguay 1y ago
There are many, many papers and projects out there about tuning foundation models on various types of medical imaging data, and many organized efforts to produce large medical imaging datasets to feed that training. This stuff is well-known in the trenches and can improve on the older, smaller CNNs in some ways, but not in a way that's produced any step change in automated capabilities. People are certainly working on it!
bko 1y ago
> I think it still remains to be seen what a 1T+ parameter transformer trained specifically for radiology will do Does image processing of something like this scale with parameters? It makes sense that language continues to scale as the vector space is huge. Even models that generate images scale as the problem space is so large. But here there are only so many pixels in the image and they are a lot more uniform. You likely can't have 1T images to train on, so wouldn't the model just overfit and basically memorize every image it has ever seen?
- throwuxiytayq 1y ago
  If the meat machine can learn it, then the silicon machine can learn it.

airstrike 1y ago

So will it cost me less than $1.5k the next time I need an x-ray?

agos 1y ago
of course not, that would be silly. it will cost $1.5k + the AI analysis fee

qgin 1y ago

People get tripped up by thinking "there is a subset of what I do that only humans can do and so that means AI will not eliminate my profession entirely and my job is safe". Let's assume for now that it's true that AI can't do a certain subset of your work. Your profession won't be eliminated from the earth, that's true. But if 80% of your work can be done by AI, 80% of your work will be done by AI. There will still be humans kept around for that remaining 20%, but fewer of them will be needed.

nradov 1y ago
The demand for radiologists is effectively infinite. Right now the healthcare system is supply constrained. If AI tools reduce the work of reading an image then more imaging studies will be ordered. Also, many radiologists do interventional procedures directly with patients. We're a long, long way from being able to significantly automate that work.
- seesthruya 1y ago
  This is 100% true and very insightful, but not obvious to people who don't work in healthcare.
- SoftTalker 1y ago
  So I recently needed an MRI to evaluate a rotator cuff injury. Because MRIs are expensive, insurnace required that I do 6 weeks of physical therapy first to see if that would resolve it. Is this because the imaging process is so expensive, or because the radiologist who reads the image is so expensive? If demand for an expensive imaging process increases, it will get even more expensive.
  - nradov 1y ago
    MRI machines and radiologist reads are both expensive. These are among the largest areas of medical spending for most health plans (and those costs are largely passed on to subscribers and employers). Many rotator cuff injuries do improve to some extent with targeted physical therapy. You can accomplish a lot by strengthening the stabilizer muscles, and by learning how to set your shoulder joint in the correct position before taking a load. So the insurer's position is reasonable for most cases. If the injury is more severe then your doctor can submit a request to skip the normal step therapy process.

qwertox 1y ago

TL;DR: “Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.”

epistasis 1y ago

For a more technical dive into startups that have been chugging along with AI in pathology for ~decade, check out this: https://www.owlposting.com/p/what-happened-to-pathology-ai-companies https://www.owlposting.com/p/what-happened-to-pathology-ai-c... > One pathology AI founder told me that it wasn’t hospitals or diagnostic labs that showed the most promise. It was R&D groups within Big Pharma. Those scientists and executives wanted new tooling. They were often sitting on massive internal datasets, had real budget allocated to experimental tech, and — critically — had a clear ROI if your model helped shave months off a study or more precisely target the right patient cohort. Most importantly, pharma didn’t care as much about the regulatory headaches, as they weren’t using your model to diagnose patients

abhishaike 1y ago
<3
yread 1y ago
Really nice article, thanks for sharing. With digital pathology ai startups going bust left and right it's quite an accurate analysis. One thing it's missing in the "product" path is preanalytical differences - same tissue processed in different labs can produce wildly different pixels.

MrBuddyCasino 1y ago

Reminder that according to https://openai.com/index/healthbench/ https://openai.com/index/healthbench/, not only do current AI models provide far better recommendations than doctors, but even if doctors know the AI recommendations as a starting point, human revision does not bring any improvement. Honestly this doesn't surprise me, considering the quality of the average doctor.

pseudocomposer 1y ago

I formerly worked at https://corista.com https://corista.com, dealing specifically with integrations with AI pathology algorithm vendors like https://www.qritive.com https://www.qritive.com and https://visiopharm.com https://visiopharm.com. (Note: we dealt specifically with microscopy rather than radiology.) I really think we were doing things the “right way” before I left: providing AI analyses from various vendors as overlays for slides, and being able to pre-flag slides with obvious cases of cancer or other infection. (These analyses typically being custom algorithms provided by vendors through APIs we collaborate on, not LLM output.) With things like this simply built into an existing, established pathology platform, I’m pretty confident a team of 4-5 pathologists could do the work of 6-8, with better quality output, similar to how Copilot and other tooling speeds us up as developers. At $300-400k/yr/doctor, that’s considerable savings (and the opportunity to allocate more doctors in specialties that aren’t easily automated). However, for lots of reasons, it seems the market doesn’t necessarily agree with me on the value potential of this approach in this field (which can certainly be a self-fulfilling prophecy).

ProllyInfamous 1y ago
>the value potential of this approach in this field As a medical school dropout, I think the largest "negative" in value assessment would be the inability to shift malpractice liability over to a human practitioner (i.e. can you insure a company's faulty AI/LLM?) — yet today I just read a HN article on insurers offering a new product: insuring chat/LLM fuck-ups. As exciting as this all is, I'm still so glad I didn't become a radiologist!
- seesthruya 1y ago
  For what it's worth, I almost dropped out of medical school in my second year to go back to a career in tech. It was a close thing. Still, I have enjoyed my time in radiology. It is insanely challenging to do well, and has been very rewarding.
  - ProllyInfamous 1y ago
    I think I could have been happy with the physician lifestyle, but I knew I wouldn't make good life decisions surviving/getting through residency. I am glad to have survived (e.g. career change). I've spent two decades as an electrician, and as the story goes: it is what it is... but also what you make of it. An incredible Good Fortune keeps me from having to work full-time, but as I've entered my forties I know I want to get in an air conditioned office (before too long). Perhaps I'll first move to a country where healthcare isn't tied to full-time employment. Best of luck to you in your Interpretations.

jmward01 1y ago

If there is one thing that NN can do amazingly well it is pattern recognition. The biggest blockers to adoption are the EHR providers, regulations and just inertia. It will happen, the only question is when and to a small degree, how.

nradov 1y ago
EHR vendors aren't the major blocker here. Most of them have pretty good APIs now (at least for reading USCDI data from patient charts). But the data quality as entered by users is often crap: when you're training it's going to be garbage in / garbage out unless you put enormous efforts into cleansing. And the raw images are often not in the EHR but rather in a separate PACS.
- jmward01 1y ago
  From what I have seen trying to build out new features into this area, EHR vendors often act like monopolies blocking a lot of competition by controlling any data exchange. The vendor lock-in is strong and very harmful to improving process and, ultimately, harms patient care.
  - nradov 1y ago
    What specifically are they blocking? The most popular EHRs from vendors like Epic and Oracle have extensive interoperability APIs for multiple open industry standards. In general you can read all USCDI data pretty easily. Writing data is more limited, for understandable reasons. And if you want to distribute a SMART on FHIR app then you'll have to go through some sort of approval process. The strong vendor lock-in is unavoidable given how deeply an EHR gets embedded into any provider organization. No matter what regulators and standards bodies do, it will never be easy to switch.

kazinator 1y ago

AI is not easily going to "steal" any properly gatekept jobs that require a licensed practitioner. Machine learning technology can be used to search for artifacts in medical diagnostic images that humans might miss. The flagged situations still have to be interpreted by a radiologist.

joshdavham 1y ago

> “People should stop training radiologists now,” Geoffrey Hinton said, adding that it was “just completely obvious” that within five years A.I. would outperform humans in that field. I'm starting to notice a strong trend where non-domain experts will confidently assert that X field will be replaced by AI.

godelski 1y ago
Isn't this a common trend with CS? It seems like there's just constant over hype that drives booms and crashes, even to such a degree that if something was there it can't even be built. We have billionaires and hundred millionaires that made their money on VR, blockchain, and Web3. The wealth comes even when no product does and that seems like a big issue that's being exploited
calf 1y ago
Hinton is a domain expert in AI, case in point Scott Aaronson cites AI revolutionizing protein-folding research, putting many protein-folding academics out of jobs. I'd argue that AI experts and CS theorists are in a better position to speak about technological change than the domain experts who are trained/selected mainly to practice the status quo.
- a_bonobo 1y ago
  > putting many protein-folding academics out of jobs. Has this actually happened? I'd wager it's more like in OP's article, AI as a helper that speeds up research, enabling bigger or different research questions. Reliable prediction not of protein shape, but of protein-protein or protein-mRNA interactions, for example.