This study was designed to develop methods to improve radiologists' ability to detect and diagnose breast cancer. We evaluated the ability of a feature-analysis method to help radiologists merge judgements constructively from two rather disparate breast imaging tests. To accomplish these goals, we developed a list of perceptual features and quantitated the importance of each in the diagnosis of patients having both diaphanography (Test 1) and mammography (Test 2). Then, two decision aids were developed: One was a checklist of the critical diagnostic visual features from both tests that also assisted readers in rating these features numerically. The second was a computer-based classifier that assisted readers in merging the assessments of the two tests into one overall diagnostic probability. The value of these aids was assessed by comparing radiologists' accuracy in reading a set of proven cases in their standard fashion with their accuracy when reading in an enhanced mode, utilizing the checklist and computer classifier. When Test 1 was read adjunctively with Test 2, use of the decision aids led to a significant improvement in accuracy (p = .013) over the unenhanced, combined readings. For Test 1 alone, the aids led to a significant improvement over its low level of unenhanced reading (p = .046). For Test 2 alone, the enhancements provided little gain in accuracy over an already high level of performance on the full case set (p = .081), although significant gains were realized on the most difficult ones. We conclude that methods to aid standardization and merging of feature-based judgements can improve radiologists performance on complex diagnostic tasks.