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Content-based image retrieval (CBIR) has the potential to provide medical doctors with a powerful resource to help make accurate diagnoses. To aid in diagnosis, a CBIR system must retrieve similar images from the same (unknown) disease class as the patient. We have implemented a CBIR system that first predicts the disease class of the query image and then retrieves the n images nearest to the query image from the pool of images with the predicted disease class. With the cooperation of residents/radiologists at Indiana University Medical Center and the Department of Radiology at the University of Wisconsin we have recently completed an evaluation of our system. The results show that when using our system, the diagnostic accuracy of the group increased on average by 32% over diagnosis without any reference materials.