ArticleNature medicine2022
Swarm learning for decentralized artificial intelligence in cancer histopathology.
Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 2 of them syntheses that pooled it.
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Who cites it
92 citing papers in PubMed, 2 syntheses or guidelines pooled it, 202 citations in OpenAlex.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Review
- Swarm Learning for the Detection of Middle Cerebral Artery Occlusion On CT Angiography.Clinical neuroradiology · 2026Article
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.NEJM AI · 2026Article
- Review
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data.NPJ systems biology and applications · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Artificial Intelligence and Radiomics in Molecular Oncology Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach.BMC oral health · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile.Scientific reports · 2025Article
- Decentralized, privacy-preserving surgical video analysis with Swarm Learning.medRxiv : the preprint server for health sciences · 2025Article
- Histopathological evaluation of abdominal aortic aneurysms with deep learning.Diagnostic pathology · 2025Article
32 more citing papers are in PubMed but not listed here.
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Authors and funding
27 authors at 10 institutions in 3 countries.
Funding
Abstract
Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer.
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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.