ArticleNature machine intelligence2021
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Both ends of artificial intelligence impacting privacy: a review of violation and protection.Frontiers in artificial intelligence · 2026Pooled it
- Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.Cell reports. Medicine · 2024Pooled it
- Exploring the limits of localization: federated model stacking improves hospital-level prediction in a national research network.NPJ digital medicine · 2026Article
- Automatic detection of multi-site oral squamous cell carcinoma based on privacy-preserving federated learning.BMC oral health · 2026Article
- Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.Nature communications · 2025Article
- Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx.Computational and structural biotechnology journal · 2025Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Orbital learning: a novel, actively orchestrated decentralised learning for healthcare.Scientific reports · 2024Article
- Identifying autism spectrum disorder from multi-modal data with privacy-preserving.Npj mental health research · 2024Article
- The unintended consequences of artificial intelligence in paediatric radiology.Pediatric radiology · 2024Article
- Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.iScience · 2023Article
- Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.Journal of digital imaging · 2023Article
- Differentially private knowledge transfer for federated learning.Nature communications · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.Cancers · 2022Article
- New Generation Federated Learning.Sensors (Basel, Switzerland) · 2022Article
- Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.NPJ digital medicine · 2022Article
- Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.21st century pathology · 2022Article
- Artificial intelligence in oncologic imaging.European journal of radiology open · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
46 authors.
Funding
Abstract
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.
Identifiers
What OpenQuestion holds
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.