Abstract :
[en] Federated Learning (FL) offers a promising solution to the
dual challenges of data privacy and multi-institutional collaboration in
medical imaging. However, despite strong benchmark performance, FL
models rarely reach routine clinical deployment. We hypothesize that this
“last-mile” gapstemsfromamisalignmentbetweencurrentFLevaluation-
focused on technical metrics-and the priorities of value-based healthcare
(VBHC). We conduct a structured gap analysis comparing current FL
practices with VBHC principles and emerging regulatory frameworks.
Seven critical deployment axes are identified; six show high-severity gaps,
and one a medium - severity gap.Supporting literature is limited:only one
axis is backed by strong evidence, three by moderate, one by weak, and
two by very weak reviews. Based on these findings and insights from real-
world pilots, we propose a practical roadmap to align FL development
with clinical and regulatory expectations. By identifying key evidence
gaps and outlining actionable next steps, this work aims to inform trans-
lational strategies and support the deployment challenges addressed by
the BRIDGE Workshop.
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