Techniques that may enhance diagnostic accuracy in clinical settings were tested in the context of mammography. Statistical information about the relevant features among those visible in a mammogram and about their relative importances in the diagnosis of breast cancer was the basis of two decision aids for radiologists: a checklist that guides the radiologist in assigning a scale value to each significant feature of the images of a particular case, and a computer program that merges those scale values optimally to estimate a probability of malignancy. A test set of approximately 150 proven cases (including normals and benign and malignant lesions) was interpreted by six radiologists, first in their usual manner and later with the decision aids. The enhancing effect of these feature-analytic techniques was analyzed across subsets of cases that were restricted progressively to more and more difficult cases, where difficulty was defined in terms of the radiologists' judgements in the standard reading condition. Accuracy in both standard and enhanced conditions decreased regularly and substantially as case difficulty increased, but differentially, such that the enhancement effect grew regularly and substantially. For the most difficult case sets, the observed increases in accuracy translated into an increase of about 0.15 in sensitivity (true-positive proportion) for a selected specificity (true-negative proportion) of 0.85 or a similar increase in specificity for a selected sensitivity of 0.85. That measured accuracy can depend on case-set difficulty to different degrees for two diagnostic approaches has general implications for evaluation in clinical medicine. Comparative, as well as absolute, assessments of diagnostic performances--for example, of alternative imaging techniques--may be distorted by inadequate treatments of this experimental variable. Subset analysis, as defined and illustrated here, can be useful in alleviating the problem.