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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
by 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 agoThey’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 agoI don't think the UK NHS has a single centralized PACS or EHR.
- physicsguy 1y agoIt'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 agoI 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"Takes AGI?" It's the same argument people used to make about NLP. By now, it's proven wrong.
- danjl 1y agoAnother way of saying this that might not trigger your alarm bells is that even a perfect image analyzer is not enough to replace a radiologist. The job consists of much more than just analyzing images.
- justlikereddit 1y ago>Unfortunately, there is no comparably vast database of medical images. Even a tiny hospital with radiology services will produce many thousands of images with accompanying descriptions every year. And you are allowed to anonymize and do research on these things in many places as neither image nor accompanying description is a personal identifier. So this is yet another Hinton-ish prediction, any time soon radiologist are going dodo. This time LLMs will crack the nut that image recognition have failed at for 20 years. Where LLMs have succeeded is in doing hot takes that miss the mark, they should be really good at cornering the "prematurely predicting demise of radiologist"-market