For more than a decade, radiologists have used traditional computer aided detection systems to read mammograms, but mainly because of a low computer specificity may not improve their screening performance, according to several studies. The breakthrough in deep learning techniques has boosted the performance of machine learning algorithms, also for breast cancer detection in mammography. The objective of this study was to determine whether radiologists improve their breast cancer detection performance when they concurrently use a deep learningbased computer system for decision support, compared to when they read mammography unaided. A retrospective, fully-crossed, multi-reader multi-case (MRMC) study was designed to compare this. The employed decision support system was Transpara™ (Screenpoint Medical, Nijmegen, the Netherlands). Radiologists interact by clicking an area on the mammogram, for which the computer system displays its cancer likelihood score (1-100). In total, 240 cases (100 cancers, 40 false positive recalls, 100 normals) acquired with two different mammography systems were retrospectively collected. Seven radiologists scored each case once with, and once without the use of decision support, providing a forced BI-RADS® score and a level of suspiciousness (1-100). MRMC analysis of variance of the area under the receiver operating characteristic curves (AUC), and specificity and sensitivity were computed. When using decision support, the AUC increased from 0.87 to 0.89 (P=0.043) and specificity increased from 73% to 78% (P=0.030), while sensitivity did not significantly increment (84% to 87%, P=0.180). In conclusion, radiologists significantly improved their performance when using a deep learningbased computer system as decision support.