AI bias in fetal ultrasound linked to image quality, not just representation

Researchers have developed a new framework to identify and disentangle intersectional bias in medical AI, specifically examining fetal ultrasound models. The framework combines unsupervised slice discovery, factor-wise analysis, and targeted intersectional evaluation to address performance disparities that can arise even with adequate data representation. A case study on fetal weight estimation using over 94,000 ultrasound images revealed that pixel spacing, often adjusted for factors like high BMI or low gestational age, significantly impacted model performance, highlighting the need for acquisition-aware fairness evaluations. AI

影响 Introduces a novel methodology for detecting and mitigating intersectional bias in medical AI applications, crucial for equitable healthcare.

排序理由 Academic paper presenting a new framework for analyzing bias in medical AI. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• deep learning
• fetal ultrasound
• Hadlock

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