[naveen99]

I get bored by more esoteric statistics terms in epidemiology, but accuracy has a simple enough mathematical formula: https://www.lexjansen.com/nesug/nesug10/hl/hl07.pdf

(True positives + True Negatives) / number of all tested

Similar concept comes up in measuring accuracy of computerized image segmentation, where you ignore the true negatives

true positive / (true positive + false positive + false negative)

where it is called intersection over Union (IOU).

I can’t ever remember the names, and just rebuild whatever metric I care about in terms of true vs false and positive vs negative.

Applying all this to the real world is tough because of the over fitting problem. Even if you got the test to be 100% accurate in your tested population, it doesn’t mean it won’t be wrong on the next person it tests. Generalization is hard. So doctors have to guess based on their understanding of the tested and untested population and the sensitivity and specificity of the test. You can go meta and give the doctor a sensitivity and specificity also.