It's been a while since I took statistics, but isn't the whole point of <thing> testing that it needs to have a high Bayes factor, and to be confident that said

It's been a while since I took statistics, but isn't the whole point of <thing> testing that it needs to have a high Bayes factor, and to be confident that said Bayes factor is high? In this case, and my lack of understanding in both bio and stats is showing here, we're trying to develop a test for breast cancer. An ideal test will have high sensitivity and specificity, and a tight confidence interval for both of those numbers. This way we can be confident that a positive/negative test actually moves our priors meaningfully in either direction. I guess my question/comment is more on the fact that I don't see how any of the results shown actually translate, as the headline suggests, into a cancer test of high power. The priors for any kind of cancer is pretty low from what I can find, so we need high power tests in both the positive and negative direction to meaningfully effect health outcomes. I can't find any CI numbers in the paper, which may just be me not reading it closely enough, but it doesn't help my confidence.