Methods and Impact for Using Federated Learning to Collaborate on Clinical Research

Article Multidisciplinary Sciences

Swarm Learning for decentralized and confidential clinical machine learning

Stefanie Warnat-Herresthal et al.

Summary: The study introduces Swarm Learning, a decentralized machine-learning approach that integrates medical data globally while complying with local privacy regulations. Using over 16,400 blood transcriptomes and more than 95,000 chest X-ray images, the research shows that Swarm Learning classifiers outperform those developed at individual sites.

NATURE (2021)

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Article Biochemistry & Molecular Biology

Federated learning for predicting clinical outcomes in patients with COVID-19

Ittai Dayan et al.

Summary: Federated learning, a method for training artificial intelligence algorithms while protecting data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes globally. The study showed improved predictive accuracy and generalizability, setting the stage for wider applications in healthcare.

NATURE MEDICINE (2021)

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Article Computer Science, Artificial Intelligence

End-to-end privacy preserving deep learning on multi-institutional medical imaging

Georgios Kaissis et al.

Summary: PriMIA is a free, open-source software framework for privacy-preserving medical image analysis, performing well in instance testing and preventing data disclosure.

NATURE MACHINE INTELLIGENCE (2021)

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Article Computer Science, Information Systems

Federated Learning for Healthcare Informatics

Jie Xu et al.

Summary: With the increasing availability of healthcare data, the fragmented and private nature of these data poses challenges for generating robust results. Federated learning technology shows promise in protecting privacy and connecting disparate healthcare data sources. This survey reviews the applications and potential of federated learning in the biomedical space.

JOURNAL OF HEALTHCARE INFORMATICS RESEARCH (2021)

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Review Clinical Neurology