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