That is, until the model encounters something it hasn't quite seen before. Hence the need to keep humans in the loop...
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Reading CT scans is incredibly hard. I have no doubt that with sufficient training data, deep learning will eventually be better at it than trained specialists.
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In my thesis I worked on segmenting both liver & hepatic tumor using NN. It took me weeks to learn how to recognize liver in a CT. On the other hand, a FCN model learned to recognize liver using 30 training images in few seconds
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The humans reading them currently seem to focus purely on the things they are asked to look for
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What ground truth do you use to surpass human performance for this specific task?
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I have been reading CT scans for 20 years, wrote several textbooks, and am a professor at a little Boston hospital. I see something I have never seen before *every day*. Replacement is a massive engineering challenge that lacks commercial logic for the foreseeable future.
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Do you see potential for "assistive" intelligence in your work? Tech that would highlight interesting spots in a CT scan and let you annotate scans or otherwise "teach" it to recognize things you would want to be sure to notice?
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Yes. Or preferably, to give a strong recommendation that can be overruled only with cause.
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That to me is antithetical to good medical practice. We function in a complex environment with lots of nuance. The quickest way to alienate a referrer is to make hard recommendations based on imaging alone.
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Agree with the first sentence. A good report answers a clinical question and recommends further management - integrating available multi-modality imaging/clinical information. ML may well have a role but not in the simplistic way you suggest. Join me for a reporting list anytime!
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