alt Hacker NewsFalse positives are important because of Bayes theorem. Even a test that’s 99% sensitive in a high incidence population can be indistinguishable from noise in a low incidence population.
If it has a 1% false positive rate but the incidence is 1%, the vast majority of the positives are false. Then you have to deal with the consequences, including invasive procedures for further diagnosis.
If you’re searching for tens or hundreds of low incidence conditions in the general population at a time it’s absolutely worthless because basically every positive is a false positive. At that point save the scan fee, spin a wheel of body parts and go get a biopsy of that.
This is why doctors are confused why companies are offering periodic full body scans in normal people. They only test people who are high risk or symptomatic to confirm a suspected diagnosis. That extra signal is what makes the test useful.
Go down to the medical diagnosis section for a worked example.
https://en.wikipedia.org/wiki/Bayes'_theorem
Regarding cancers every human has all sorts of weird lumps that are generally meaningless.
In order for this to not be a boondoggle it would have to be spectacularly accurate to a degree previously unheard of. Just from a statistics perspective.
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As a person experiencing UV sensitive skin, I’ve had multiple wheel-spin biopsies which turned out benign as expected, and at least once a year I find a weird looking spot I have take pictures of and promise to monitor for a bit. I don’t think there’s any reason this kind of stuff couldn’t be extended to other cancers if non-invasive next steps were available.
As we gain more data, might we be able to find patterns in that data that we now cannot see? I'm not only thinking of these regular scans but combining it with other data sources, like maybe regular, more complete blood panels, Apple Watch data, whatever we can get our hands on. Maybe we can find data points that together have a lower false-positive rate, like lump plus increased nightly body temperature plus weight loss.