I have not been able to fully digest this paper yet, and medical data is not my speciality. It is interesting but not surprising that models appear able to dete

I have not been able to fully digest this paper yet, and medical data is not my speciality. It is interesting but not surprising that models appear able to determine the demographics of the patient that even radiologists are unable to. It is also not surprising that models use this to "cheat" (find demographic shortcuts in disease classification). My understanding is that doctors may unconsciously do this as well, ignoring a possible diagnosis because they don't expect a patient of a certain demographic to have a particular issue. I would expect radiologists who practice in very different demographic environments would not do as well when evaluating images another environment. At the end of the day radiology is more an art than a science, so the training data may well be faulty. Krupinski (2010) wrote in an interesting paper [1]: "Medical images need to be interpreted because they are not self-explanatory... In radiology alone, estimates suggest that, in some areas, there may be up to a 30% miss rate and an equally high false positive rate ... interpretation errors can be caused by a host of psychophysical processes ... radiologists are less accurate after a day of reading diagnostic images and that their ability to focus on the display screen is reduced because of myopia. " I would hope datasets included a substantial amount of images that were originally mis-classified as a human. [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881280/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881280/