Hi,

One practical application of fake news is creating realist looking educational material.
For instance, in radiology a frequent test is to have a plastic phantom with small, slightly different patches, and radiologist have to find these patches. This is kind a test for the image quality what the scanner produces. But if you know how these phantoms look like, it is easier to guess.
Therefore, a realistic looking slight, artificial lesion/tumor would be beneficial training doctors.
Similarly, not only training doctors, but testing medical AI could benefit from deepfakes.
If the fake tumor is indistinguishable from real ones, than the medical AI should recognize both types.
Obviously, using deep fakes, there is a much larger control on the environment, so besides the classic tests (software testing, performance test against human observers, etc.), another layer of robustness-test can be added. (E.g. FDA could have an automated service for compliance test. And having different generated dataset every time can prevent from overfitting the method on a standardized test set.)