{
  "id": 1985,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11632-4",
  "title": "Fairness in federated medical imaging: a systematic review through the dual fairness lens",
  "summary": "Federated learning (FL) enables multi-institutional collaboration in medical imaging while preserving patient privacy, yet its fairness landscape remains fragmented: existing methods predominantly address either collaboration fairness (equitable performance across institutions) or group fairness (equitable outcomes across demographic subgroups), but rarely both. In this systematic review, we adopt dual fairness —the joint satisfaction of both dimensions—as the analytical lens for organizing and ",
  "authors": null,
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-05T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/1985",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11632-4",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}