Acquiring multiple images of the same patient (e.g., mediolateral oblique and craniocaudal view mammograms) can, in principle, help improve diagnostic accuracy. We investigated theoretically, in the context of computer-aided diagnosis (CAD), four methods of combining multiple computer outputs obtained from multiple images of the same patient: taking the average, the median, the maximum, or the minimum of the individual assessments. We assumed that multiple computer outputs for each patient are equally accurate and that they can be transformed monotonically to the same pair of truth conditional normal distributions. We found that both the average and the median always produce improved area under the ROC curve (AUC) compared to single-view images, and that the average always performs better than the median. Furthermore, the maximum and the minimum can also produce improved AUCs and can outperform the average under certain situations, but in other situations they can produce worse results than single-view images. Moreover, except for the median, each method can be the best-performing method under specific conditions. Finally, as the strength of correlation between image pairs increases, the maximum and the minimum tend to perform the best more often whereas the average is less often the best performer.