The role of federated learning models in medical imaging
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Aggregating model updates from diverse patient data across institutions improves generalizability, which is essential for predictive accuracy in diverse populations.
“Improved Model Generalization: Aggregating model updates from diverse patient data across institutions improves generalizability, essential for predictive accuracy in diverse populations [47].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [47]
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References 9 · Cited by 25
- Authors
- Kwak, L., & Bai, H.
- Affiliations
- Johns Hopkins University (US)
- Venue
- Radiology Artificial Intelligence
- Published
- 2023-05-01
- Volume
- 5
- Issue
- 3
- Pages
- e230136-e230136
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC10245176
- OpenAlex
- W4378840507
- Cited by
- 25
- References
- 9
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 5 · 2023: 4
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology Artificial Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- federated learning, diagnostic imaging, artificial intelligence, privacy-preserving machine learning, distributed training
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