I am assuming you are referring to the expert system used for infectious diseases. They performed well, but could not adapt to the constantly changing guidelines, medications. Data input was inefficient back in the days of terminals. It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject.

The deep neural net visual diagnostics are different. They are learned on pure pixels as we would from photons striking our retina, signals traveling all the way back to our visual cortex for learning. There is no assembling of thousands of rules here and therefore the system is less brittle.

These new systems get more powerful with more data given to them. Expert systems required humans to craft rules from the data, and therefore required constant maintenance and is fallible to human error.

So yes, some of the barriers are definitely technological in nature.