Does a term exist for this concept in statistics? Suppose you have a dataset where the covariates are biomedical information for a given patient (e.g. height, weight, blood type, etc.) and the response variable is whether the patient has a certain disease. You are interested in making a classification model that will help screen patients in advance for this disease (i.e. save time and money).
Suppose you are able to create a decision tree that produces decent accuracy - but the "rules" produced by the decision tree are not very "helpful".
For example: if height between (150, 180 cm) and weight between (150, 200 lbs) then disease = true.
Suppose this rule proves to be a very accurate rule (i.e. when this condition is true, patients almost always have the disease) - but it's deemed too vague by the doctors to actually use. Instead, the doctors prefer to spend money/resources and physically screen patients.
Is there a term for this in statistics? An accurate model which is deemed too arbitrary to use?

BLACK-BOX MODEL
That is the closest that I can think to what you describe. However, a "black-box model" is not necessarily useless. While many applications want to know how the model works, there are some situations where just getting the right answer is the crucial aspect.
The name comes from the idea of throwing data into a black box, so that seeing in is impossible, and it pops out an answer; the inability to see inside the black box to see how it makes its predictions is critical. One popular example of a black-box model is a neural network, which can give extremely accurate predictions (look at MNIST performance) but is hard to interpret.
