The unreasonable usefulness of deep learning in medical image datasets

Medical data is horrible to work with. In medical imaging, data stores (archives) operate on clinical assumptions. Unfortunately, this means that when you want to extract an image (say a frontal chest x-ray), you will often get a folder full of other images with no easy way to tell them apart. Depending on the manufacturer, you might end up with horizontally or vertically flipped images. They might have inverted pixel values. The question is, when dealing with a huge dataset (say, 50-100k images), how do you find these aberrations without having a doctor look at all of them?