English(EN) Risk Control Certifies Rates, Not Magnitudes: An Audit of Selective Fetal Biometry

AI对胎儿生物测量学的审计揭示了错误率控制的局限性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

本研究论文介绍了一种审计产前超声生物测量学中选择性预测的方法，重点关注头围（HC）测量。研究发现，虽然统计风险控制可以认证错误率，但无法防止大误差幅度的出现。通过限制可接受测量值的解剖学范围，显著降低了管道系统的最坏情况误差，证明了将领域知识纳入AI驱动的医疗工具的重要性。 AI

影响强调了AI医疗工具中稳健的错误控制对于确保患者安全的需求。

排序理由学术论文发表于arXiv。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Zhengshuai Yang, Linna Jiang, Changyun Li, Guihu Zhao · 2026-11-05 05:00

风险控制认证费率而非幅度：对选择性胎儿生物测量学的审计

arXiv:2611.05851v1 Announce Type: new Abstract: Prenatal ultrasound biometry must return a head circumference (HC) and refuse when the image cannot support one. Selective prediction trades coverage against error, but its finite-sample guarantees bound the rate of gross errors, no…