{
  "id": 9920,
  "url": "https://doi.org/10.1038/s41746-025-02280-z",
  "title": "Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications",
  "summary": "Differential privacy (DP) is a prominent technique for protecting sensitive patient data in medical deep learning (DL), yet deploying it without compromising clinical utility or equity remains challenging. This scoping review synthesizes applications of DP in medical DL across centralized and federated settings. A structured search identified 74 eligible studies published through March 2025. Across modalities and tasks, DP, especially via DP-SGD, can maintain clinically acceptable performance un",
  "authors": "Marziyeh Ranjbar‐Mohammadi, Mohsen Vejdanihemmat, Mahshad Lotfinia, Mirabela Rusu, Daniel Truhn, Andreas Maier",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-01-03T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:04.098Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9920",
  "original_url": "https://doi.org/10.1038/s41746-025-02280-z",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}