{
  "id": 5240,
  "url": "https://arxiv.org/abs/2604.26991v2",
  "title": "People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation",
  "summary": "Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources. Prior work on AI fairness and human-AI cooperation, including learning to defer (L2D) and learning to complement (L2C), typically addresses these problems in isolation. We propose People-Centred Medical Image Analysis (PecMan), a framework for fairness-aware human-AI co-operative class",
  "authors": "Zheng Zhang, Milad Masroor, Cuong Nguyen, Tahir Hassan, Yuanhong Chen, David Rosewarne et al.",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-28T22:13:36.000Z",
  "fetched_at": "2026-07-14T16:31:35.576Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5240",
  "original_url": "https://arxiv.org/abs/2604.26991v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}