We have developed a method for improving the accuracy of image-based diagnosis by providing the radiologist with image-reading and decision-making aids. The image-reading aid is a checklist of visual features to guide the radiologist through a systematic assessment of the features of a detected abnormality. The decision aid is a computer-based statistical decision rule that accepts the feature assessments made by the radiologist, and returns an advisory estimate of the probability that disease is present. I discuss the steps involved in developing and testing these aids, and present illustrative examples of their successful application in mammography. I describe other possible benefits of a feature-based diagnostic system in improving report standardization, training of radiologists, quality assurance, and automated report-writing. Finally, I discuss potentially greater improvements in diagnostic accuracy that may be realized through the future development of hybrid systems in which a radiologist and computer interact, and systems combining features from multiple tests or sources.