ArticleScientific reports2020
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 420 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
420 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- From promise to practice: digital health through the lens of neonatal care.Lancet regional health. Americas · 2026Review
- Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study.Nature biomedical engineering · 2026Article
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- Predictive Models for Toxicities after CAR T-cell Therapy: Challenges and Opportunities.Blood cancer discovery · 2026Review
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Comment on "Privacy-preserving multicenter validation of the RMODscore".Journal of cancer research and clinical oncology · 2026Article
- From pixels to prediction: improving imaging through AI in rheumatology.EULAR rheumatology open · 2026Review
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.Journal of medical Internet research · 2026Article
- Evaluating the Performance of Mammogram-Based AI Risk Model in Predicting Subsequent Breast Cancer in Women with a Prior History of Breast Cancer.Journal of clinical medicine · 2026Article
- The Continuity Trap in Data Science Health Research.Journal of medical Internet research · 2026Article
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Stage-Aware Swin-Enhanced nnU-Net v2 for Robust Polyp Segmentation in Collaborative Endoscopic Visual Sensing.Sensors (Basel, Switzerland) · 2026Article
360 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.