• Sonori@beehaw.org
    link
    fedilink
    arrow-up
    4
    ·
    10 months ago

    I believe it was from a study on detecting Tuberculosis, but unfortunately google isn’t been very helpful for me.

    The problem with that would be that people in poorer areas are more at risk from TB is not a new discovery, and a model which is intended and billed as detecting TB from a scan should ideally not be using a factor like hospital is old and poor to determine if a scan has diseased tissue, given that intrinsically means your model is more likely to miss it in patients at better hospitals while over-diagnosing it in poorer ones, and that of course at risk people can still go to newer hospitals.

    A Doctor will take risk factors into consideration, but would also know that just because their hospital got a new machine doesn’t mean that their patients are now less likely to have a potentially fatal disease. This results in worse diagnosis, even if it technically scores better with the training set.

    • ericjmorey@programming.devOP
      link
      fedilink
      arrow-up
      3
      ·
      10 months ago

      A Doctor will take risk factors into consideration

      Unfortunately we see that the data doesn’t support this assumption. Poor populations are not given the same attention by doctors. Black populations in particular receive worse healthcare in the US after adjusting for many factors like income and family medical history.

      • Sonori@beehaw.org
        link
        fedilink
        arrow-up
        2
        ·
        edit-2
        10 months ago

        It’s unfortunately not certain that they will take such measures with their patients even though most try, and indeed ethic discrepancies are one of the things likely to be made worse with machine learning given that there is often little thought or training data given to them, but age of the hospitals machine is not a good proxy for risk factors. It might be statistically corralled, the actual patients risk isn’t. Less at risk people may go to a cheaper hospital, and more at risk people might live in a city which also has a very up to date hospital.