Jefferies gives IBM Watson a Wall Street reality check
&gt; Jefferies pulls from an audit of a partnership between IBM Watson and MD Anderson as a case study for IBM’s broader problems scaling Watson. MD Anderson cut its ties with IBM after wasting $60 million on a Watson project that was ultimately deemed, “not ready for human investigational or clinical use.”<p>Well, can&#x27;t say I&#x27;m surprised.  I used to work on that project a few years ago, basically the idea was that Watson would look at a patient&#x27;s medical record, figure out what medications they&#x27;re on, what symptoms they had, etc. and cross-reference all that with the medical knowledge it had ingested from vast amounts of medical literature.  In theory, Watson could figure out what medications the patient should or should not be using, a proper course of treatment, etc.<p>There were two major problems:<p>First, it turns out your medical record is mostly written in narrative form, i.e., &quot;John Smith is a 45 year old male...&quot;, &quot;Patient is taking X mg of Y twice daily&quot;, &quot;Patient was administered X ml of Y on 3&#x2F;1&#x2F;2016&quot;, etc.  In other words, there&#x27;s basically no structured data, so just figuring out the patient&#x27;s stats, vitals, medications, and treatment dosages was an adventure in NLP.  All that stuff was written in sentence form, and of course how things were written depended on who wrote it in the first place.  It was really, really hard to make sure Watson actually had correct information about the patient in the first place.<p>Second, all that medical literature that was being ingested?  Regular old, don&#x27;t-know-anything-about-medicine programmers were the ones writing the rules the manipulating the data extracted via NLP.  Well guess what, if you&#x27;re not a domain expert you&#x27;re bound to get things wrong.<p>Put those two things together and we would frequently get recommendations that were wildly incorrect, but that&#x27;s to be expected when you get garbage input being fed into algorithms written by people who aren&#x27;t domain experts.