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A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals

Abstract:: Background
Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an arti... Expand abstract
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Soltan, A. S., Thakur, A., Yang, J., Chauhan, A., D'Cruz, L. G., Dickson, P., Soltan, M. A., Thickett, D. R., Eyre, D. W., Zhu, T., & Clifton, D. A. (2024). A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals. Lancet Digital Health, 6(2), E93–E104.

MLA Style

Soltan, AS, et al. “A Scalable Federated Learning Solution for Secondary Care Using Low-Cost Microcomputing: Privacy-Preserving Development and Evaluation of a COVID-19 Screening Test in UK Hospitals.” Lancet Digital Health, vol. 6, no. 2, 2024, pp. E93–E104.

Chicago Style

Soltan, AS, A Thakur, J Yang, et al. 2024. “A Scalable Federated Learning Solution for Secondary Care Using Low-Cost Microcomputing: Privacy-Preserving Development and Evaluation of a COVID-19 Screening Test in UK Hospitals.” Lancet Digital Health 6 (2): E93–E104.
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Files:: Soltan_et_al_2024_A_scalable_federated.pdf

(Preview, Version of record, pdf, 1.7MB, Terms of use)

Publisher copy:: 10.1016/S2589-7500(23)00226-1

Authors

+ Soltan, AS More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Oncology
Role:: Author
ORCID:: 0000-0003-2391-5361

+ Thakur, A More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author
ORCID:: 0000-0002-7006-1947

+ Yang, J More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ Chauhan, A More by this author

Role:: Author

+ D'Cruz, LG More by this author

Role:: Author

More authors...

+ The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund More from this funder

Grant:: 0009350

Publisher:: Elsevier
Journal:: Lancet Digital Health More from this journal
Volume:: 6
Issue:: 2
Pages:: E93-E104
Publication date:: 2024-01-24
Acceptance date:: 2023-10-30
DOI:: 10.1016/S2589-7500(23)00226-1
EISSN:: 2589-7500

Language:: English
Pubs id:: 1555911
Local pid:: pubs:1555911
Deposit date:: 2023-10-31
ARK identifier:: ark:/29072/ora_12e2da0ce5094d20bc0d07e5b5563eae

Terms of use

Copyright holder:: Soltan et al.
Copyright date:: 2024
Rights statement:: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Licence:: CC Attribution (CC BY)

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