Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification

This research identifies a critical failure mode in chest X-ray classification models: they systematically underdiagnose conditions in minority patient subgroups, even when overall accuracy appears acceptable. The study demonstrates that standard thresholding approaches mask performance disparities across demographic or clinical subgroups, a particularly concerning finding for healthcare AI deployment where diagnostic accuracy must be equitable across all populations.

This research identifies a critical failure mode in chest X-ray classification models: they systematically underdiagnose conditions in minority patient subgroups, even when overall accuracy appears acceptable. The study demonstrates that standard thresholding approaches mask performance disparities across demographic or clinical subgroups, a particularly concerning finding for healthcare AI deployment where diagnostic accuracy must be equitable across all populations.

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