Can AI serve as an independent second reader of mammograms? A simulation study

In this study we used a large previously built database of 2,892 mammograms and 31,650 single mammogram radiologists' assessments to simulate the impact of replacing one radiologist by an AI system in a double reading setting. The double human reading scenario and the double hybrid reading scenario (second reader replaced by an AI system) were simulated via bootstrapping using different combinations of mammograms and radiologists from the database. The main outcomes of each scenario were sensitivity, specificity and workload (number of necessary readings). The results showed that when using AI as a second reader, workload can be reduced by 44%, sensitivity remains similar (difference - 0.1%; 95% CI = -4.1%, 3.9%), and specificity increases by 5.3% (P<0.001). Our results suggest that using AI as a second reader in a double reading setting as in screening programs could be a strategy to reduce workload and false positive recalls without affecting sensitivity.