5 ms·

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

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.
- Workaccount2 1y ago
  You are right, but I meant it more as a total radiologist, scans the images and can put it into medical context.
  - anon373839 1y ago
    Unfortunately, VLMs don’t seem to work. Even the best ones (eg, Gemini 2.5 Pro) hallucinate to a laughable degree. I’ve been seeing them make up large-scale features in images that absolutely no human would ever mistake. What does work is narrow AI trained to do very specific tasks (such as medical image segmentation). But I don’t think that type of AI will benefit much from massive scaling.
CGMthrowaway 1y ago
"The radiologist will be with you soon" also gets used pretty loosely in this article, since a radiologist is not someone you are likely to speak with. They sit in their home office in Montana clicking through images all day, and send thru their diagnosis to a different practitioner at the office you are visiting.
- fred_is_fred 1y ago
  Which means the field has a larger issue since they can just as easily sit in the Philippines or Brazil or India.
  - seesthruya 1y ago
    There's a reason they can't.
    - lostlogin 1y ago
      Why not? Where I work scans are reported overseas. The radiologists are qualified in the country they are reporting for however.
      - seesthruya 1y ago
        Ok, I should have been more clear. A radiologist practicing in the USA can live anywhere in the world, but they must be trained in the USA, licensed in the state(s) where they read from, credentialed at the hospitals or facilities they read from, and carry medical malpractice insurance. The point being, hospitals can't throw cheap Indian or Filipino radiologists at the worklist.
        jfengel 1y ago
        Can a certified radiologist farm out work to cheap assistants in other countries, and then put their stamp on the result?
darth_avocado 1y ago
Medical records remain a walled garden mostly because of HIPAA. Unlike all the AI development that has managed to skirt the copyright law to train large models, training a 1T+ parameter transformer on a large enough dataset will need a lot of consumers to give up their medical records.
- 98codes 1y ago
  When it comes to test results, does HIPAA prevent anonymization, such that all would be provided is test input & eventual result?
  - SOTGO 1y ago
    From a summary on HHS.gov it says "De-Identified Health Information. There are no restrictions on the use or disclosure of de-identified health information." Maybe someone with more knowledge could expand on the limitations of what counts as "De-Identified" but I think that might work. I followed the reference and nothing in the CFR jumps out at me, but I'm not a lawyer so who knows.
    - nradov 1y ago
      HHS has published clear guidelines on de-identification. https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html https://www.hhs.gov/hipaa/for-professionals/special-topics/d...
- SoftTalker 1y ago
  China won't have an issue with it.
  - maeil 1y ago
    The US won't have an issue with it. You haven't realized what the US has become.
    - 1y ago
getnormality 1y ago
For a while it seemed that "AI" topics were treated as niche and news articles used relatively specific terms of art like deep learning or machine learning. Somehow ChatGPT changed that and now everything is just AI.
- wormius 1y ago
  I do my best to filter ai topics here using ublock, and it's blocked about a third of the posts, and yet every day there's a new variation, whether a new technique/technology, a new product (open source or commercial), etc... that I have to add to the filter. It's really offputting. Yet apparently this is the only site besides lobste.rs that has programming related content that is of any depth that I know of. I really wish hacker news had the ability to tag posts and let me filter (and tag them generally not have to get specific on each new buzzword or product).
  - paulddraper 1y ago
    Why do you want to read about tech, but specially non-AI tech?
    - namaria 1y ago
      I'll speak for myself, for me it's illustrated by the spam sketch from Monty Python. It crowds out everything, including my mental space. The hype and disconnect from reality make it intriguing to my brain so I am very likely to end up reading the comments about it, but it's usually a waste of my time. Software does what it does and my opinions (or anyone else's) just don't matter. I should focus my time on learning and building instead of trying to understand hype.
- SpicyLemonZest 1y ago
  ChatGPT changed that because it, along with other current advances, brought AI out of its niche and into widespread mainstream usage. The laboratory the source article covers (https://www.mayo.edu/research/labs/radiology-informatics/overview https://www.mayo.edu/research/labs/radiology-informatics/ove...) publishes some of the technical detail you're interested in, but in 2025 it's also interesting to people less familiar with theoretical computer science. The doctors quoted in the source article, using programs that can calculate kidney volume or detect blood clots, don't necessarily want or need to know the difference between "artificial intelligence" and "deep learning".

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.
Workaccount2 1y ago
You are right, but I meant it more as a total radiologist, scans the images and can put it into medical context.
- anon373839 1y ago
  Unfortunately, VLMs don’t seem to work. Even the best ones (eg, Gemini 2.5 Pro) hallucinate to a laughable degree. I’ve been seeing them make up large-scale features in images that absolutely no human would ever mistake. What does work is narrow AI trained to do very specific tasks (such as medical image segmentation). But I don’t think that type of AI will benefit much from massive scaling.

CGMthrowaway 1y ago

"The radiologist will be with you soon" also gets used pretty loosely in this article, since a radiologist is not someone you are likely to speak with. They sit in their home office in Montana clicking through images all day, and send thru their diagnosis to a different practitioner at the office you are visiting.

fred_is_fred 1y ago
Which means the field has a larger issue since they can just as easily sit in the Philippines or Brazil or India.
- seesthruya 1y ago
  There's a reason they can't.
  - lostlogin 1y ago
    Why not? Where I work scans are reported overseas. The radiologists are qualified in the country they are reporting for however.
    - seesthruya 1y ago
      Ok, I should have been more clear. A radiologist practicing in the USA can live anywhere in the world, but they must be trained in the USA, licensed in the state(s) where they read from, credentialed at the hospitals or facilities they read from, and carry medical malpractice insurance. The point being, hospitals can't throw cheap Indian or Filipino radiologists at the worklist.
      - jfengel 1y ago
        Can a certified radiologist farm out work to cheap assistants in other countries, and then put their stamp on the result?

darth_avocado 1y ago

Medical records remain a walled garden mostly because of HIPAA. Unlike all the AI development that has managed to skirt the copyright law to train large models, training a 1T+ parameter transformer on a large enough dataset will need a lot of consumers to give up their medical records.

98codes 1y ago
When it comes to test results, does HIPAA prevent anonymization, such that all would be provided is test input & eventual result?
- SOTGO 1y ago
  From a summary on HHS.gov it says "De-Identified Health Information. There are no restrictions on the use or disclosure of de-identified health information." Maybe someone with more knowledge could expand on the limitations of what counts as "De-Identified" but I think that might work. I followed the reference and nothing in the CFR jumps out at me, but I'm not a lawyer so who knows.
  - nradov 1y ago
    HHS has published clear guidelines on de-identification. https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html https://www.hhs.gov/hipaa/for-professionals/special-topics/d...
SoftTalker 1y ago
China won't have an issue with it.
- maeil 1y ago
  The US won't have an issue with it. You haven't realized what the US has become.
  - 1y ago

getnormality 1y ago

For a while it seemed that "AI" topics were treated as niche and news articles used relatively specific terms of art like deep learning or machine learning. Somehow ChatGPT changed that and now everything is just AI.

wormius 1y ago
I do my best to filter ai topics here using ublock, and it's blocked about a third of the posts, and yet every day there's a new variation, whether a new technique/technology, a new product (open source or commercial), etc... that I have to add to the filter. It's really offputting. Yet apparently this is the only site besides lobste.rs that has programming related content that is of any depth that I know of. I really wish hacker news had the ability to tag posts and let me filter (and tag them generally not have to get specific on each new buzzword or product).
- paulddraper 1y ago
  Why do you want to read about tech, but specially non-AI tech?
  - namaria 1y ago
    I'll speak for myself, for me it's illustrated by the spam sketch from Monty Python. It crowds out everything, including my mental space. The hype and disconnect from reality make it intriguing to my brain so I am very likely to end up reading the comments about it, but it's usually a waste of my time. Software does what it does and my opinions (or anyone else's) just don't matter. I should focus my time on learning and building instead of trying to understand hype.
SpicyLemonZest 1y ago
ChatGPT changed that because it, along with other current advances, brought AI out of its niche and into widespread mainstream usage. The laboratory the source article covers (https://www.mayo.edu/research/labs/radiology-informatics/overview https://www.mayo.edu/research/labs/radiology-informatics/ove...) publishes some of the technical detail you're interested in, but in 2025 it's also interesting to people less familiar with theoretical computer science. The doctors quoted in the source article, using programs that can calculate kidney volume or detect blood clots, don't necessarily want or need to know the difference between "artificial intelligence" and "deep learning".