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?

Could this be an issue of precision and recall?
You are writing "when this condition is true, patients almost always have the disease" and call this accuracy. But usually one/I would not call that accuracy. The English Wikipedia has a large table in the "Precision and recall" article https://en.wikipedia.org/wiki/Precision_and_recall Here accuracy is, - as usual: (TP + TN) / (P + N), so your accuracy could approximately be rephrased as "when this condition is true, patients almost always have the disease AND when the condition is not true 'patients' almost never have the disease."
One should think that e.g., the precision or the recall is too low for the doctors. It may also be that the prevalence of the disease is very low, so that you can reach apparently high accuracy with just declaring everyone healthy. In screeening, perhaps the "False omission rate" is all what the doctors care about.
