Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
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
Record details
Published: 3 January 2026
Source: OpenAlex
Category: Research
Topics: Bias & fairness · Privacy · Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (3 January 2026), “Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications,” evidence record 9920, https://ethics.ai/record/9920 (originally published by OpenAlex).
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