AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial
Mené en Espagne à partir de données portant sur 31 301 femmes ayant bénéficié en routine entre 2022 et 2024 de mammographies numériques ou de tomographies de synthèse numériques, cet essai évalue la non infériorité, par rapport à une double lecture standard en aveugle et du point de vue de la charge de travail des radiologues, du taux de détection de lésions cancéreuses ainsi que du taux de rappel, d'une stratégie de triage partiellement automatisée utilisant l'intelligence artificielle pour identifier les cas à faible risque et assister la double lecture
Artificial intelligence (AI) systems have been demonstrated to improve the accuracy of screening mammograms. Here this prospective, paired, noninferiority clinical trial evaluated whether AI could safely reduce workload by excluding low-risk exams from radiologist reading. Between March 2022 and January 2024, 31,301 women were included in the trial and underwent routine mammograms. Two reading strategies were applied in parallel: standard double-blind reading and partially autonomous AI-supported screening, where cases classified by AI as low risk were assessed as normal and the rest were double read with AI support. The primary outcomes were radiologist workload, cancer detection rate and recall rate. In the AI strategy, radiologist workload was 63.6% lower; the cancer detection rate was 15.2% higher (95% confidence interval 6.6%, 24.4%), increasing from 6.3 of 1,000 to 7.3 of 1,000, P < 0.001; and the recall rate was not noninferior and was 14.8% higher (95% confidence interval 9.0%, 20.6%). Subanalyses by modality highlighted a similar workload reduction in digital mammography (−62.1%) and digital breast tomosynthesis (−65.5%). However, in digital mammography, the cancer detection rate increased by 1.6 of 1,000 and the recall rate by 1.3%, while both remained stable in digital breast tomosynthesis. These results demonstrate the feasibility of a partially automated AI workflow in breast cancer screening, avoiding human reading of studies classified as low risk. ClinicalTrials.gov: NCT04849776.
Nature Medicine , article en libre accès, 2026