This is more true than people realize. My wife and I got full body MRI scans.

Both showed "possible" medical issues. My though was "Great, I have a baseline, in two years I'll get another one and compare".

My wife on the other hand got a bit obsessed about her results and had what was probably an unnecessary procedure to biopsy something, which turned out to be benign.

I suppose you could argue that another way...better safe than sorry...but the stress that is caused by known uncertainty vs unknown uncertainty can be too much.

Not a doctor, but the overdiagnosis concern is at the intersection of three phenomena:

1. Imaging is expensive, just in dollars and time, even without analysis

2. Imaging is not without impact -- CT scans, especially full body scans, expose the body to ionizing radiation

3. Imaging is time-consuming

The net result of these means that full body scans are difficult to interpret. If a doctor given a patient complaint suspects a condition that is sufficiently non-specific that a full-body scan is required, then the scan will be interpreted through the lens of the known progress of the differential diagnosis. And typically these scans must be done without a healthy baseline, so minor findings in this context might have significant diagnostic power when combined with history or other findings.

But on a healthy patient, minor findings are very likely to be noise, because we don't have a great deal of experience with scans of healthy people, for the reasons above.

This technology, if it pans out, gives a way of inverting 1, 2, and 3. If every healthy doctor visit includes one of these scans, then the medical field gets experience interpreting them, and more importantly, when new symptoms occur, previous scans can be compared to determine whether a particular finding in the current scan is new or has changed.

If something like this became commonplace (and accurate enough), then it could be fantastic for research: enabling us to map out what variations are common and which aren't in a way that hasn't previously been feasible.

In the dark ages of machine learning, researchers tried to fit natural language into a defined, human-curated taxonomy.

It kinda worked, for a reasonable amount of stuff; but failed quite a lot of the time, and there's an extremely long tail of things that would have been pragmatically impossible to ever address with that method--indeed, without adopting an entirely new, unsupervised model of language, continuous in places where the old way was discrete.

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