AI RESEARCH
A Framework for Exploring and Disentangling Intersectional Bias: A Case Study in Fetal Ultrasound
arXiv CS.LG
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ArXi:2605.02942v1 Announce Type: new Bias in medical AI is often framed as a problem of representation. However, in image-based tasks such as fetal ultrasound, performance disparities can arise even when representation is adequate, because predictive accuracy depends strongly on image quality. Image quality is shaped by acquisition conditions and operator expertise, as well as patient-dependent factors such as maternal body mass index (BMI), all of which may correlate with sensitive graphic features.