Mammography is the gold standard for breast cancer screening in the vast majority of the world, but it is known to be less accurate for women with dense breasts. To improve cancer detection accuracy, supplemental ultrasound and Magnetic Resonance Imaging (MRI) screening have been recently introduced and are actively recommended for high-risk populations by many agencies. This chapter studies the value of supplemental tests in non-high-risk populations using a partially observable Markov decision process model alongside a simulation model. A numerical study using these models driven by clinical data reveals that supplemental tests may not cause any meaningful improvement in the quality-adjusted life expectancy for non-high-risk women, and they may indeed be harmful if used routinely after biennial or annual mammographies for non-high-risk women. However, they are associated with significant improvements in overall cancer detection rate, the time to detect cancer, fraction of in situ cases that deteriorate to the invasive stage as well as the fraction of women who die with undiagnosed cancer. While MRI is generally more effective than ultrasound on several performance metrics, it also suffers from significantly increased false positives, hindering its viability for this population.