Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.

Journal: Transfusion
PMID: 39610333

Abstract

BACKGROUND: Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intelligence (AI) into healthcare requires understanding these data infrastructures and addressing challenges such as data availability, privacy, and governance. Federated learning (FL), a decentralized AI training approach, addresses these challenges by allowing models to learn from diverse datasets without data leaving its source, thus ensuring privacy and security are maintained. This report introduces FL and discusses its potential in transfusion medicine and blood supply chain management.

Authors

  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Antoine Lewin
    Medical Affairs and Innovation, Héma-Québec, Montreal, Quebec, Canada.
  • Shuoyan Ning
    Michael G. DeGroote Centre for Transfusion Research, McMaster University, Hamilton, Ontario, Canada.
  • Marianne Waito
    Integrated Supply Chain Planning, Canadian Blood Services, Ottawa, Ontario, Canada.
  • Michelle P Zeller
    Michael G. DeGroote Centre for Transfusion Research, McMaster University, Hamilton, Ontario, Canada.
  • Alan Tinmouth
    Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada.
  • Andrew W Shih
    Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.

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