• Kichae
    link
    fedilink
    English
    arrow-up
    9
    ·
    9 months ago

    Eh. Depends on which tech is being used and how. For a lot of things, relatively basic ML models purposefully trained do a pretty good job, and are, in fact, limited by the diagnoses in the training data. But more generalized “AI” tools seem rather… questionable.

    Like, you can train a SVM on fMRIs to compare structures in the brain between patients diagnosed with bipolar disorder and those that are not diagnosed with it, and it will have an accuracy rate on new patients basically equal to the accuracy rate of the doctors who did the diagnosing in the training set. But you’ll have a much harder time creating a model that takes in fMRIs and reports back answers to the question of “which brain disease or abnormality do I have?”

    This stuff works much closer to advertised when it’s narrowly defined and purpose built, but the people making and funding this work want catch-all doctor replacements, because of course they do, because there’s way more money in charging hospitals and patience 10% less than a doctor’s salary than there is in providing tools that make doctors’ efforts in diagnosing specific illnesses easier.

    Or, at least there is if you can pull it off.

    • rho50@lemmy.nz
      link
      fedilink
      arrow-up
      1
      ·
      9 months ago

      Precisely. Many of the narrowly scoped solutions work really well, too (for what they’re advertised for).

      As of today though, they’re nowhere near reliable enough to replace doctors, and any breakthrough on that front is very unlikely to be a language model IMO.

      • Kichae
        link
        fedilink
        English
        arrow-up
        2
        ·
        9 months ago

        And they should no more replace doctors in the future than x-ray machines did in the past. We should never want them to.