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. Polarity classification is still indexing.
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FairLogue shows that intersectional disparities in two clinical prediction tasks are largely consistent with 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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FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
FairLogue shows that intersectional disparities in two clinical prediction tasks are largely consistent with randomized group membership.