{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:57:41Z","timestamp":1792454261699,"version":"4.5.0"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009192","name":"Alberta Innovates","doi-asserted-by":"publisher","award":["AI Advance \u2010 NSERC Alliance 2022\/23"],"award-info":[{"award-number":["AI Advance \u2010 NSERC Alliance 2022\/23"]}],"id":[{"id":"10.13039\/501100009192","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000014","name":"Canadian Blood Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN\u20102022\u201002999"],"award-info":[{"award-number":["RGPIN\u20102022\u201002999"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transfusion"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Health data comprise data from different aspects of healthcare including administrative, digital health, and research\u2010oriented 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Discussion<\/jats:title><jats:p>FL can offer significant benefits in transfusion medicine by enhancing predictive analytics, personalized medicine, and operational efficiency. Predictive models trained on diverse datasets by FL can improve accuracy in forecasting blood transfusion demands. Personalized treatment plans can be refined by aggregating patient data from multiple institutions using FL, reducing adverse reactions and improving outcomes. Operational efficiency can also be achieved through precise demand forecasting and optimized logistics. Despite its advantages, FL faces challenges such as data standardization, governance, and bias. Harmonizing diverse data sources and ensuring fair, unbiased models require advanced analytical solutions. Robust IT infrastructure and specialized expertise are needed for successful FL implementation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>FL represents a transformative approach to AI development in healthcare, particularly in transfusion medicine. By leveraging diverse datasets while maintaining data privacy, FL has the potential to enhance predictions, support personalized treatments, and optimize resource management, ultimately improving patient care and healthcare efficiency.<\/jats:p><\/jats:sec>","DOI":"10.1111\/trf.18077","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T08:44:28Z","timestamp":1732869868000},"page":"22-28","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Privacy\u2010preserving federated data access and federated learning: Improved data sharing and <scp>AI<\/scp> model development in transfusion medicine"],"prefix":"10.1111","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4803-0984","authenticated-orcid":false,"given":"Na","family":"Li","sequence":"first","affiliation":[{"name":"Department of Community Health Sciences, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Department of Computing and Software McMaster University  Hamilton Ontario Canada"},{"name":"Michael G. DeGroote Centre for Transfusion Research McMaster University  Hamilton Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-4198","authenticated-orcid":false,"given":"Antoine","family":"Lewin","sequence":"additional","affiliation":[{"name":"Medical Affairs and Innovation, H\u00e9ma\u2010Qu\u00e9bec, Montreal  Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7675-4058","authenticated-orcid":false,"given":"Shuoyan","family":"Ning","sequence":"additional","affiliation":[{"name":"Michael G. DeGroote Centre for Transfusion Research McMaster University  Hamilton Ontario Canada"},{"name":"Department of Pathology and Molecular Medicine McMaster University  Hamilton Ontario Canada"},{"name":"Department of Medicine, Michael G. 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