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It generally means model running in parallel with the actual system. It is not just about being able to store data, but about being able to mirror (and sometimes predict/replicate) exactly what that system is doing.

In current software parlance, this is often used in stupidly trivial ways, but digital twins have a long and important history and function

https://en.wikipedia.org/wiki/Digital_twin

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There's a huge difference between modeling only the stimulus/response, and modeling the full interior and exterior dynamics. "Digital twin" only refers to the latter.

I wholeheartedly agree that it's a safe bet that very very few digital twin projects achieve anything close to what they propose. More typically it is a flashy label to stun management. I actually worked on digital twins -- I came into ML via physics -- and once you start digging, you find the rabbit hole is deep enough that absent massive government grants or angel corporate investors you will never get anywhere close to what could be a called a digital twin.

To use this terminology on biological systems seems more like a very lofty goal than anything realizable with current technology.

Not that there's any issue with lofty goals.

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