.png){kind=icon}
.png){kind=icon}
Return
From Data to Value: Gaps in Federated Learning Evaluation for Clinical Deployment in Medical Imaging
.png){kind=icon}
PRE
AI
B
J
H
V
DOI：10.1007/978-3-032-05663-4_5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 gap stems from a misalignment between current FL evaluation-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 highseverity 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 translational strategies and support the deployment challenges addressed by the BRIDGE Workshop.
Keywords:
Federated Learning
Value-Based Healthcare
Global regulatory frameworks
Quality Management System
Trustworthy AI
Journal
Organization
Cited Papers