[maz1b]

I had to check the date after seeing the headline, and again after opening the page. Thought it was April Fools.

Regardless, as a doctor and full stack engineer, I'm looking forward to learning more about their methodologies, their approaches, but I don't think this is going to be displacing MRIs or remotely close, based off the cursory initial glance. If their vision is to be able to provide end users with more actionable data with some kind of "low fidelity" medical imaging data that is somewhere above zero and or standard imaging and high fidelity modalities like CT/MRI, then this could be somewhat interesting.

Not a radiologist and not medical advice. Just my two cents.

[999900000999]

Is the idea to use AI magic to detect cancer and other bad things?

I could imagine this getting cheap enough that your local gym has one and you get checked once every 3 months.

Curing cancer is one of the only things I’d take a pay cut to do.

[arcticbull]

Bayes theorem mostly. False positives rates are extremely important. I mean so are false negatives. So just, like, accuracy.

[SpicyLemonZest]

False positive rates are extremely important in the medical system as it exists today, where most scans will come without a known baseline and doctors cannot prescribe "biweekly scans for the next 6 weeks to see what changes". If we can achieve the kind of imaging abundance they're imagining (which I don't know how to evaluate based on their short post), I think false positives become much less of an issue, at least in the context of cancer where malignancy is the only problem.

[arcticbull]

False 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.

[ajmurmann]

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.

[SpicyLemonZest]

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.

[arcticbull]

If you’re UV sensitive and at a higher risk then you’re already in a high incidence population making the tests valuable statistically speaking. That test is wildly more accurate for you than it would be for me, and even still you’ve been the unfortunate recipient of many false positives. There’s no reason for me or most people to do that since practically 99% or more of the positive tests would be wrong.

Biopsies are expensive, waste time, hospital resources and carry risks of infection and scarring that do not net out positively for people who aren’t in your risk group.

Getting a totally random positive doesn’t put you into a higher incidence category so whatever follow up test you take will be just as inaccurate as the first one.

The reason to avoid them is the tests would be a waste of time, statistically, and expose you to a bad risk-reward profile.

If you knew apriori 99% of the positive tests are false positive why are you taking the test?

It’s literally just math. Sometimes the right thing for you on average is to do nothing, which feels bad, but it’s still the right thing to do.

[dualvariable]

Timing is also important. I can predict cancer with 100% success, because everyone will get cancer, unless they die of something else first.

[nxobject]

> Curing cancer is one of the only things I’d take a pay cut to do.

Send an email to this head-and-neck oncologist's lab. I saw a talk he gave at a Chicago-area national lab on open-source models for identifying malignancies in scanned pathology slides, and was smitten.

https://voices.uchicago.edu/pearsonlab/

[Nikhil37475]

Fair point. Definitely not a replacement; it’s meant to bridge the data gap.