Extended Data Fig. 2: Performance of the AI system in breast cancer prediction compared to six independent readers, with a 12-month follow-up interval for cancer-positive status.
From: International evaluation of an AI system for breast cancer screening
Whereas the mean reader AUC was 0.750 (s.d. 0.049), the AI system achieved an AUC of 0.871 (95% CI 0.785, 0.919). The AI system exceeded human performance by a significant margin (ΔAUC = +0.121, 95% CI 0.070, 0.173; P = 0.0018 by two-sided ORH method). In this analysis, there were 56 positives of 408 total cases; see Extended Data Table 3. Note that this sample of cases was enriched for patients who had received a negative biopsy result (n = 119), making it a more challenging population for screening. As these external readers were not gatekeepers for follow-up and eventual cancer diagnosis, there was no bias in favour of reader performance at this shorter time horizon. See Fig. 3a for a comparison with a time interval that was chosen to encompass a subsequent screening exam.
