AI in fetal ultrasound needs calibration and explainability, review finds

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 →

• Artificial Intelligence
• CALIB-XFUS
• EU AI Act
• Fetal Ultrasound
• Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov
• FDA

• paper
• safety
• policy

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