Collective intelligence encompasses several methods for summarizing input from multiple individuals, which can often be more accurate than any one expert. In this study, investigators applied several collective intelligence algorithms to mammography interpretation. They found that aggregating the interpretations of multiple radiologists resulted in higher accuracy—fewer false positive results and more true positive results—than even the most accurate single radiologist. This work builds on earlier studies of diagnostic accuracy in imaging studies. This study has profound implications for improving diagnosis through collaboration between clinicians in real time, perhaps facilitated through technology, as a complement to the long-standing diagnostic safety strategy of morbidity and mortality conferences, which provide group feedback once a case has concluded.