Simple SummaryCurrent AI algorithms show breast cancer detection rates that are comparable to human readers, but it is not clear whether AI and humans detect cancers with similar characteristics. As these factors will influence survival, we aimed to compare the invasiveness status, histological grade, lymph node stage, and tumour size of cancers. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI indicated 85.9% and 31.7%, respectively, as suspicious. The first human reader detected 90.8% of SDCs and 7.2% of ICs. There were no differences in the detected proportion for any of the investigated prognostic factors. The AI algorithm detected more ICs. These findings imply that using AI has limited or no downstream effects on screening programmes, supporting its potential role in the double-reading workflow.AbstractInvasiveness status, histological grade, lymph node stage, and tumour size are important prognostic factors for breast cancer survival. This evaluation aims to compare these features for cancers detected by AI and human readers using digital mammography. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. Differences in prognostic features of cancers detected by AI and the first human reader (R1) were assessed using chi-square tests, with significance atp< 0.05. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI flagged 85.9% and 31.7%, respectively. R1 detected 90.8% of SDCs and 7.2% of ICs. Of the screen-detected cancers detected by the AI, 82.5% had an invasive component, compared to 81.1% for R1 (p-0.374). For the ICs, this was 91.5% and 93.8% for AI and R1, respectively (p= 0.829). For the invasive tumours, no differences were found for histological grade, tumour size, or lymph node stage. The AI detected more ICs. In summary, no differences in prognostic factors were found comparing SDC and ICs identified by AI or human readers. These findings support a potential role for AI in the double-reading workflow.