I don't find that persuasive. This is not the error I worry about. Let's say that hypothetically the model just ignores the input image 1 in 10,000 runs. This really doesn't concern me because the output will be trivially detectable incorrect nonsense that doesn't match the symptoms at all. Such a contingency is easily handled by running the image through multiple models and distilling the output, anyway.

The error I worry about is where the model uses the image and comes to an incorrect but symptom matching diagnosis. But in this hypothetical the model is less likely to do so than a doctor, so the choice is either accept the risk of the model or accept a higher risk from a doctor.