{
  "id": 8887,
  "url": "https://doi.org/10.1038/s41586-021-03583-3",
  "title": "Swarm Learning for decentralized and confidential clinical machine learning",
  "summary": "Abstract Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4,5 . Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we ",
  "authors": "Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg",
  "category": "research",
  "topics": "regulation,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2021-05-26T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:47.093Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8887",
  "original_url": "https://doi.org/10.1038/s41586-021-03583-3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}