Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
Translating intersectionality to fair machine learning in health sciences
2 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
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Intersectional fairness audits of two clinical models on All of Us found larger subgroup gaps than single-axis checks, yet counterfactuals suggested most gaps matched randomized group membership.
citing papers explorer
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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
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Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Intersectional fairness audits of two clinical models on All of Us found larger subgroup gaps than single-axis checks, yet counterfactuals suggested most gaps matched randomized group membership.