{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T05:47:36Z","timestamp":1791870456155,"version":"4.3.4"},"reference-count":109,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transfusion"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI)\/machine learning (ML) are poised to transform transfusion medicine across the vein\u2010to\u2010vein continuum, from donor recruitment to recipient outcomes. Large datasets generated from millions of donations and transfusions create opportunities for predictive modeling, precision medicine, improved safety, and operational efficiency. This review outlines key applications of AI in blood banking and transfusion medicine using a comprehensive vein\u2010to\u2010vein framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The review synthesizes perspectives from blood services and hospital\u2010based transfusion practices. Applications are discussed across donor recruitment, collections, testing, inventory management, hospital transfusion services, and education. Finally, AI safety, including ethical, regulatory, privacy, and implementation challenges, is addressed. This is a narrative, non\u2010exhaustive review; given the rapidly evolving pace of AI\/ML capabilities, this synthesis should be interpreted as a framework for ongoing surveillance rather than a fixed inventory of applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Donor\u2010focused applications include personalized recruitment, individualized iron management, and fairness auditing to reduce inequities. In supply chain operations, ML can improve demand forecasting, minimize waste, and optimize inventory. Federated learning may support multi\u2010institutional collaboration without sharing raw data. Omics and genomic applications can enable high\u2010throughput analysis, phenotype prediction, and precision transfusion support for rare blood types and alloimmunized patients. In hospitals, AI can augment hemovigilance by identifying\/predicting adverse transfusion reactions, while large language models and retrieval\u2010augmented generation may enhance decision support, patient blood management, and education through adaptive learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI holds substantial promise to improve transfusion safety, efficiency, equity, and sustainability. Realizing this potential will require robust governance, model validation across diverse populations, privacy safeguards, bias mitigation, reproducibility, regulatory oversight, and human\u2010in\u2010the\u2010loop implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/trf.70418","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T05:25:48Z","timestamp":1791869148000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The transformative power of artificial intelligence and machine learning in blood banking\/transfusion medicine: A vein\u2010to\u2010vein approach from blood collection to transfusion services: On behalf of the AABB Foundation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9653-9905","authenticated-orcid":false,"given":"Ruchika","family":"Goel","sequence":"first","affiliation":[{"name":"Department of Pathology Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Carter BloodCare  Dallas Texas USA"},{"name":"Division of Hematology\u2010Oncology, Department of Internal Medicine Simmons Cancer Institute, SIU School of Medicine  Springfield Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8247-1484","authenticated-orcid":false,"given":"Sheharyar","family":"Raza","sequence":"additional","affiliation":[{"name":"Canadian Blood Services Medical Affairs and Innovation  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1780-4470","authenticated-orcid":false,"given":"W. 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