>>This title is somewhat click bait though, because the fault is really with EMR systems and (esp) the American Healthcare system, not deep learning. Right Da

>>This title is somewhat click bait though, because the fault is really with EMR systems and (esp) the American Healthcare system, not deep learning. Right Data-mush is the cause That's my official term for it. Data that looks fantastic in format, but just the slightest peek under the covers reveals that every data-entry person is either freelancing and entering whatever is easiest/sorta-makes-sense for them, or under pressure to skew things, or the definitions of each code (and the cases where it does/doesn't apply) are ill-defined, or etc. etc. etc. and on top of this, we have the medical system insurer-provider adversarial relationship covered so well in the article. The result is a toxic brew of definition drift, unintentional errors, and intentional errors. It is not just the fringes and sub-one-percent of the edge cases, it is rotten to the core. Basically the entire data set is a complete lie. You may have the most perfect AI, but it literally has no chance against that toxic data swamp. Once again, the only winning move is to not play.