We're a medical device startup using a KB/RBR system for glucose control, and our greatest feature from a traceability/regulatory perspective is the ability to

We're a medical device startup using a KB/RBR system for glucose control, and our greatest feature from a traceability/regulatory perspective is the ability to produce a report for each and every decision made; why we made that decision based on what we know, and how that decision affects the output of the system. This can also be applied retroactively; so all we need to know is the version of the system employed and the sensor inputs given and we can fully replay the decision process. This is something that, as you state, you simply cannot do with a deep learning approach. Regulators don't love black boxes, especially when it comes to human lives. Is it highly specific and non-transferable? Yes. Did it take a decade of work to refine? Yes. But does it get the best results, even better than classical MPC/PID approaches? So far, yes. In medicine, it's results that matter, not reusability of the tech employed. Human trials are just around the corner, and we can find out with certainty if our approach gives the results we think it will.