I heard the same argument from my doctor when I wanted a blood scan.

But what's the intention? If you do a scan and then try to find everything that is wrong about you, you're 100% right, there will be false positives and unnecessary panic/medication etc.

However if you just collect data for months and years and WHEN you get a symptom you have a lot more data then we should be able to give better diagnosis faster. If we do that for long enough as humanity and there is data sharing the accuracy of the whole thing will increase a lot.

So you are certainly correct but you can also tighten up your definitions for true positives as you have more information on your false positives. There may exist additional signal as well.

To your point though I think there is a difference between collecting and evaluating additional data sources and using them as diagnostic tools.

I suppose I fundamentally disagree with the implication of your post that there is no value in gathering further data for these reasons, it would seem to suggest we’re already diagnostically optimal and could not do better with additional signal.

You've got that completely backwards. Correctly applying Bayes' theorem, if an anomaly is observed you incorporate the prior into the calculation of the posterior probability. You don't just give up and say "the prior is miniscule so the likelihood is useless".

And then, even that's not enough. Decision theory needs to be applied to decide what action to take. That means taking into account the expected QALYs, cost and inconvenience across the distribution of possible outcomes. There's a whole spectrum of possible decisions, from immediately performing surgery, performing an invasive test like a biopsy, performing other less invasive tests, scheduling a follow-up non-invasive test at a later date, or just following a regular schedule of non-invasive tests and looking for any evolution along a longer time period.

The real problem is the binary thinking of either "we think you have X" and therefore tests must be performed or "we think you don't have X" and therefore tests shouldn't be performed. If medical organizations adopted empirically grounded decision frameworks, by applying them consistently doctors would be able to see something anomalous, assess that the risk isn't high enough to warrant immediate investigation, and be protected from a lawsuit in the unlikely case it was, in fact, something. And then we could do away with this "if we look we might find something" nonsense, which is pure fallacy.

This is why you have multiple successive panels. If there's a disease that happens for 1 in 10k people, and you have a test with 1% FPR, 99 of 100 people will be false positive.

But what you can do then, is either run a more expensive, elaborate test or one that's proven to be statistically independent on the positive testing population.

FPR can even be a good thing. Let's say you have an expensive test with a very low false positive rate. Then you can mix together 100 samples, and get a test with a much worse FPR 100 times cheaper. Then you can repeat the same individually on the positive population.

This is fully automatic and you don't even think about it. Btw, this is why mass testing, and public healthcare can be better. You can amortize the cost of things across a large number of people for no disadvantage.

That's precisely where medicine is headed: personalized medicine.

You [hopefully] won't have to become a rare missed diagnosis because you didn't fit the demographic for this or that screening test.

Cost of genomic analysis is exponentially decreasing, and so much progress is happening so quickly.

Consider for example how in cardiology we advanced from ASCVD's 10-yr prognosis, to the PREVENT 30-yr prognosis. And still most providers are using the ASCVD score for their patients.

If this argument was as solid as you say, then all routine checks would be pointless.

I don't know about traditional blood testing, but a permanent implant which checks HR, pressure, glucose, temperature & oxidation would be pretty useful, not necessarily to diagnose anything, but to provide data for doctor when patient has actual symptomps.

The argument has some merits, but we should remember that, from the point of view of Bayes, you could apply the same argument to symptoms, which is only evidence. High odds of a false positive, means that you have not enough evidence, not that you have not useful information.

Testing healthy person for any illness by definition has infinite number of nines in false positive rate.

Problem is we never know who is healthy. That is why we are doing the test.

Medicine is not a statistical field. I've seen many times doctors dismissing someone "you're young, you can't have X". Although there is some truth in what you're saying: full body CT scans are on the rise now.

You can just run more tests to get increased statistical power.

Many smaht people have already pointed that out.

It's news to no one that tests are imperfect.

Do you have any concrete solution to that? Anything of value?