{
  "id": 3066,
  "url": "https://arxiv.org/abs/2607.07717v1",
  "title": "Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification",
  "summary": "In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deployment fairness problem as an audit question: after a long-tailed multi-label CXR model is converted from scores into decisions, who is missed? Across VinDr-CXR and MIMIC-CXR/CXR-LT, we use a diagnostic ladder to separate class-level long-tail losses, subgroup-aware weighting, group robustness, and threshold selection. On V",
  "authors": "Ha-Hieu Pham, Hai-Dang Nguyen, Dang P. M. Cao, Thanh-Huy Nguyen, Min Xu, Trung-Nghia Le, Ulas Bagci, Huy-Hieu Pham",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-04T10:30:32.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3066",
  "original_url": "https://arxiv.org/abs/2607.07717v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}