The future of digital health with federated learning
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Abstract
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
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The record
- Venue
- npj Digital Medicine
- Topic
- Machine Learning in Healthcare
- Field
- Computer Science
- Canadian institutions
- Institute on Governance
- Funders
- Centre For Medical Engineering, King’s College LondonEngineering and Physical Sciences Research CouncilBundesministerium für Bildung und ForschungNational Institute of Neurological Disorders and StrokeDeutscher Akademischer AustauschdienstUK Research and InnovationWellcome TrustNIH Clinical CenterNational Cancer InstituteNational Institutes of HealthU.S. Department of Health and Human Services
- Keywords
- Federated learningKey (lock)Digital healthHealth careConfidentialityHealth dataInformation privacy
- Has abstract in OpenAlex
- yes