{
  "id": 6628,
  "url": "https://arxiv.org/abs/2603.27460v1",
  "title": "Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development",
  "summary": "Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of powerful medical foundation mo",
  "authors": "Zhongying Deng, Cheng Tang, Ziyan Huang, Jiashi Lin, Ying Chen, Junzhi Ning et al.",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-29T00:46:53.000Z",
  "fetched_at": "2026-07-14T16:32:37.310Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6628",
  "original_url": "https://arxiv.org/abs/2603.27460v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}