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Three commercial artificial intelligence (AI)–based computer-aided detection systems showed elevated AI scores for screening mammograms up to 10 years before breast cancer diagnosis, demonstrating their potential use in flagging individuals for supplemental screening and earlier detection.

Machine learning (ML) is rapidly progressing in many scientific fields, including medicine.¹ For breast cancer, we are beholding the emergence of commercially available artificial intelligence (AI) systems for breast cancer detection (AI-CADe),^{2 – 8} triaging, diagnosis, and risk assessment.⁹ If the performance of such systems proves to be accurate and robust in a clinical setting, incorporating them into the screening process can significantly benefit both the hospital and the screening participants. The most common use case, AI-CADe, would reduce radiologist workload and potentially reduce the rate of missed cancers.¹⁰ Some challenges remain despite the potential impact of adopting these technologies in the screening process. Collectively these challenges are known as the AI translational gap, which is currently under special investigation by the Federal Drug Administration in the United States of America (USA).¹¹