{
  "id": 5974,
  "url": "https://arxiv.org/abs/2604.10904v1",
  "title": "Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance",
  "summary": "AI-based image reconstruction models are increasingly deployed in clinical workflows to improve image quality from noisy data, such as low-dose X-rays or accelerated MRI scans. However, these models are typically evaluated using pixel-level metrics like PSNR, leaving their impact on downstream diagnostic performance and fairness unclear. We introduce a scalable evaluation framework that applies reconstruction and diagnostic AI models in tandem, which we apply to two tasks (classification, segmen",
  "authors": "Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert, William Lotter",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T02:07:48.000Z",
  "fetched_at": "2026-07-14T16:32:11.181Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5974",
  "original_url": "https://arxiv.org/abs/2604.10904v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}