It's not clear to me that any biological system is understood at this level of detail. Any simulation is likely to be garbage-in, garbage-out. And even if it were fully understood, there's not enough computing power on the planet to model every atom and every molecule.
The map is not the territory, people. All models are wrong, but some are still useful. The ability to make simplifying assumptions is the whole point.
Digital Twins are part of every buzzword infested "Digital Transformation" effort in my industry.
There's also an implicit bias towards everything being decomposed as if it were digital and cleanly separable. Reality is much more analog and interconnected.
"There’s a quantity called “wall shear stress,” which is just a frictional force on the wall. We’ve shown that when doctors can visualize that wall shear stress at different parts of the artery, they may actually change the length of the stent that they choose. It really informs their decisions."
I'm not saying ML is useless, far from it, but this is reminding me a lot of Eric Yuan (the CEO of Zoom) talking about AI clones in meetings: https://www.youtube.com/watch?v=dKmAg4S2KeE
That's not what "digital twin" means though: it's been used to describe digital simulations of systems (like the circulatory system described in this story) for a long time: https://en.m.wikipedia.org/wiki/Digital_twin
I've seen the dumb AI clone idea called "personal digital twins". I don't like that it steals and dumbs down the existing useful term but we may be stuck with it.