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?

You’re talking about making predictions not inference, but in case of inference we talk about practical significance. Statistical significance may help you to detect an effect that “significantly” differs from zero, but the effect may be so small that is has no practical significance, there’s no practical use of it.
