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Evidence record 1985 · automatically gathered

Fairness in federated medical imaging: a systematic review through the dual fairness lens

Federated learning (FL) enables multi-institutional collaboration in medical imaging while preserving patient privacy, yet its fairness landscape remains fragmented: existing methods predominantly address either collaboration fairness (equitable performance across institutions) or group fairness (equitable outcomes across demographic subgroups), but rarely both. In this systematic review, we adopt dual fairness —the joint satisfaction of both dimensions—as the analytical lens for organizing and

Record details

Published: 5 July 2026
Source: Artificial Intelligence Review
Category: Research
Topics: Bias & fairness · Privacy · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Shared topic: Bias & fairness · Shared topic: Privacy

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How to cite this record

ethics.ai (5 July 2026), “Fairness in federated medical imaging: a systematic review through the dual fairness lens,” evidence record 1985, https://ethics.ai/record/1985 (originally published by Artificial Intelligence Review).

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