“AI promised to revolutionize radiology but so far its failing”
There are many parallels with seismic interpretation here.  Many companies&#x2F;etc keep promising to &quot;revolutionize&quot; interpretation and remove the need for the &quot;tedious&quot; work of a geologist&#x2F;geophysicist. This is very appealing to management for a wide variety of reasons, so it gets a lot of funding.<p>What folks miss is that an interpreter _isn&#x27;t_ just drawing lines &#x2F; picking reflectors.  That&#x27;s less than 1% of the time spent, if you&#x27;re doing it right.<p>Instead, the interpreter&#x27;s role is to incorporate all of the information from _outside_ the image.  E.g. &quot;sure we see this here, but it can&#x27;t be X because we see Y in this other area&quot;, or &quot;there must be a fault in this unimaged area because we see a fold 10 km away&quot;.<p>By definition, you&#x27;re in a data poor environment.  The features you&#x27;re interested in are almost never what&#x27;s clearly imaged -- instead, you&#x27;re predicting what&#x27;s in that unimaged, &quot;mushy&quot; area over there through fundamental laws of physics like conservation of mass and understanding of the larger regional context.  Those are deeply difficult to incorporate in machine learning in practice.<p>Put a different way, the role is not to come up with a reasonable realization from an image or detect feature X in an image.  It&#x27;s to outline the entire space of physically valid solutions and, most importantly, reject the non-physically valid solutions.