for clinicians who need to make a decision about a surgery

Why not use a decision analytic framework, particularly if making the decision is the goal.

Here is a simple setup (not sure if it is relevant to your case, but should illustrate what I mean)

Say you have a two-dimensional (simplifying) surface which represents an area of the brain where the

signal will likely propagate. Let’s say a surgeon has to decide where in that area she should

make an incision. Some parameter vector “colors” that region with the likely propagation pattern,

say like a weather map. Over this region, we create a grid of possible incisions. Now we need to

choose the specific one. Can we not define a loss function such that the benefit of making an

incision is offset by the risk/cost? If so, we could compute the expected value in some utility/loss

units for each incision line and pick the one with the max/min expected value?

The benefit here is that we move from the probability scale to risk/benefit scale, which is more

natural for practitioners to think about. The challenge, of course, is to specify an understandable

loss function, which I can see could be hard in this case. (In finance, it is sometimes easier,

particularly when the utility is linear in payoffs, which is often the case, in which case we can just

express everything in expected $$ lost/gained)