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<p>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, <i>P</i> &lt; 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: <a href="http://clinicaltrials.gov/ct2/show/NCT04849776">NCT04849776</a>.</p>

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AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月19日

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  • Esperanza Elías-Cabot,
  • Sara Romero-Martín,
  • José Luis Raya-Povedano,
  • Alejandro Rodríguez-Ruiz,
  • Marina Álvarez-Benito

摘要

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.