Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Open accessAbstract
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.
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Deep convolutional neural networks have been shown to give excellent performance in imaging.
“where deep convolutional neural networks have been shown to given excellent performance.26”
From Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture (2024) · cites this paper as [26]
CycleGANs, trained on unpaired contrast and non-contrast CT scans, have been used to generate synthetic non-contrast or contrast CT scans, showing improvements in COVID-19 diagnosis.
“In medicine, a model architecture called 'CycleGANs' , trained on unpaired contrast and non-contrast CT scans, has been used to generate synthetic non-contrast or contrast CT scans 121 , with this approach showing improvements, for instance, in COVID-19 diagnosis 122”
From Multimodal biomedical AI (2022) · cites this paper as [122]
Coupling federated learning with homomorphic encryption to train a model to predict a COVID-19 diagnosis from chest CT scans resulted in the aggregate model outperforming all of the locally trained models.
“A recent study demonstrated the potential of coupling federated learning with homomorphic encryption to train a model to predict a COVID-19 diagnosis from chest CT scans, with the aggregate model outperforming all of the locally trained models 122”
From Multimodal biomedical AI (2022) · cites this paper as [122]
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Self-citationPrivacy-Preserving Biometric Verification With Handwritten Random Digit String
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FedPC: An Efficient Prototype-Based Clustered Federated Learning on Medical Imaging
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Medical Data as a Key Asset in the Digital Health Era: A Framework for Challenges and Strategies
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Attacks classification and data privacy protection in cloud-edge collaborative computing systems
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Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning
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Federated Learning for Privacy-Preserving AI in Healthcare
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- Authors
- Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., Shu, C., Zou, X., Huang, R., Zhang, C., Liu, X., Tu, D., Xu, C., Zhang, W., Wang, X., Chen, A., Zeng, Y., Yang, D., Wang, M.-W., Holalkere, N., Halin, N. J., Kamel, I. R., Wu, J., Peng, X., Wang, X., Shao, J., Mongkolwat, P., Zhang, J., Liu, W., Roberts, M., Teng, Z., Beer, L., Sanchez, L. E., Sala, E., Rubin, D. L., Weller, A., Lasenby, J., Zheng, C., Wang, J., Li, Z., Schönlieb, C., & Xia, T.
- Affiliations
- Tongji Hospital (CN)
- University of Cambridge (GB)
- Huazhong University of Science and Technology (CN)
- Wuhan Union Hospital (CN)
- Wuhan Children's Hospital (CN)
- Wuhan Blood Center (CN)
- Tufts University (US)
- Johns Hopkins University (US)
- Stanford Medicine (US)
- Central Hospital of Wuhan (CN)
- Mahidol University (TH)
- The University of Texas MD Anderson Cancer Center (US)
- AstraZeneca (United Kingdom) (GB)
- Stanford University (US)
- Wuhan University of Science and Technology (CN)
- Corresponding author
- Bai, Xiang
- Wang, Xi
- Zheng, Chuansheng
- Wang, Jianming
- Li, Zhen
- Schönlieb, Carola
- Xia, Tian
- ORCID
- Bai, Xiang
- Wang, Hanchen
- Xu, Yongchao
- Gan, Jiefeng
- Fan, Ziwei
- Yang, Fan
- Ma, Ke
- Yang, Jiehua
- Bai, Song
- Zou, Xinyu
- Huang, Renhao
- Zhang, Changzheng
- Liu, Xiaowu
- Tu, Dandan
- Xu, Chuou
- Zhang, Wenqing
- Wang, Xi
- Chen, Anguo
- Yang, Dehua
- Wang, Ming-Wei
- Holalkere, Nagaraj
- Halin, Neil J.
- Kamel, Ihab R.
- Wu, Jia
- Shao, Jianbo
- Mongkolwat, Pattanasak
- Zhang, Jianjun
- Roberts, Michael
- Teng, Zhongzhao
- Beer, Lucian
- Sanchez, Lorena E.
- Sala, Evis
- Rubin, Daniel L.
- Weller, Adrian
- Lasenby, Joan
- Li, Zhen
- Schönlieb, Carola
- Xia, Tian
- Venue
- Nature Machine Intelligence
- Published
- 2021-12-15
- Volume
- 3
- Issue
- 12
- Pages
- 1081-1089
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC10805468
- OpenAlex
- W3213704721
- Cited by
- 75
- References
- 54
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 24 · 2023: 22 · 2022: 7
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-04-08)
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- AstraZeneca
- Wellcome Trust
- Alan Turing Institute (777826)
- Cambridge Philosophical Society
- Leverhulme Trust
- Royal Society
- European Commission (777826, HORIZON2020)
- National Natural Science Foundation of China (81771801, 61703171)
- Cambridge Trust
- National Research Council of Thailand (25/2561)
- Thammasat University
- National Institutes of Health
- Engineering and Physical Sciences Research Council (EP/T017961/1, EP/V025279/1, EP/N014588/1, EP/T003553/1, EP/S026045/1)
- National Cancer Institute (U01CA242879)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 diagnosis, federated learning, privacy-preserving machine learning, chest CT, distributed training, medical data security, model interpretability, cross-institutional collaboration, radiologist comparison
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