{
  "id": 9020,
  "url": "https://doi.org/10.1038/s41746-021-00431-6",
  "title": "Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study",
  "summary": "Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalitie",
  "authors": "Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2021-03-29T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:47.101Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9020",
  "original_url": "https://doi.org/10.1038/s41746-021-00431-6",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}