{
  "id": 6596,
  "url": "https://arxiv.org/abs/2604.09656v1",
  "title": "Fairboard: a quantitative framework for equity assessment of healthcare models",
  "summary": "Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across patient subgroups -- are rare. Here, we evaluate the equity of 18 open-source brain tumour segmentation models across 648 glioma patients from two independent datasets (n = 11,664 model inferences) along distinct univariate, Bayesian multivariate, spatial, and representational dimensions. We find that patient identity consistently explains more perfo",
  "authors": "James K. Ruffle, Samia Mohinta, Chris Foulon, Mohamad Zeina, Zicheng Wang, Sebastian Brandner et al.",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T11:02:31.000Z",
  "fetched_at": "2026-07-14T16:32:37.308Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6596",
  "original_url": "https://arxiv.org/abs/2604.09656v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}