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AI in fetal ultrasound needs calibration and explainability, review finds

By PulseAugur Editorial · [1 sources] ·

A systematic review of 78 studies published between 2015 and 2026 examined the use of explainable AI and uncertainty quantification in fetal ultrasound plane classification. While AI models achieved a pooled balanced accuracy of 0.93, only a small fraction reported on calibration or selective prediction. The review proposes a new reporting framework, CALIB-XFUS, to ensure AI systems in this high-risk medical domain are properly calibrated, explained, and fair, aligning with regulatory expectations from bodies like the FDA and EU. AI

IMPACT Ensures AI systems in high-risk medical applications meet regulatory standards for safety and reliability.

RANK_REASON Systematic review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI in fetal ultrasound needs calibration and explainability, review finds

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Systematic review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Gustav Olaf Yunus Laitinen-Fredriksson Lundstr\"om-Imanov, Ozkan Gunalp ·

    Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review

    arXiv:2601.00990v3 Announce Type: replace-cross Abstract: Fetal ultrasound is the cornerstone of antenatal care, and accurate recognition of a small set of standard anatomical planes underpins biometry, growth surveillance, and detection of structural anomalies. Deep learning cla…

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