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August 11, 2025

Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges

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Authors

JHJuntao HuZYZhengjie YangPWPeng Wang

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Overview

This survey uncovers frameworks for privacy and security in medical imaging, suggesting improvements for accuracy and efficiency.

Key Points

  • Federated learning enhances medical image analysis by preserving patient privacy while enabling model training across multiple sites, and addressing data scarcity issues.
  • The survey categorizes methodologies based on training, architecture, and unlearning, with emphasis on unique demands in handling diverse imaging modalities.
  • Assessment incorporates regulatory compliance with HIPAA and GDPR, along with balancing technical rigor against practical clinical challenges.
  • Findings highlight the importance of privacy and security, while also advocating for accuracy and efficiency in AI-driven healthcare solutions.

Cite This Study

Hu et al. (2025) studied this question.

synapsesocial.com/papers/68af2ef6cf1dd9ea359e71c8https://doi.org/10.53941/tai.2025.100010
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Federated Learning Models for Privacy-Preserving Medical Image Analysis2025 · 1 citations
  2. 2Federated Learning in Healthcare: A Privacy-Preserving Approach to Medical AI2025
  3. 3Federated Learning for Large Models in Medical Imaging: A Comprehensive Review2025
  4. 4Federated Learning for Secure and Privacy-Preserving Medical Collaboration Across Multi-Cloud Healthcare Systems2024 · 2 citations
  5. 5Federated Learning in Healthcare: From Research to Real-World Deployment2026 · 8 citations