{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:42:02Z","timestamp":1790660522412,"version":"4.1.0"},"reference-count":63,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T00:00:00Z","timestamp":1774137600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T00:00:00Z","timestamp":1774137600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vox Sanguinis"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Transfusion medicine generates enormous volumes of data across the vein\u2010to\u2010vein continuum, spanning donor characteristics, laboratory testing, component manufacturing, logistics and recipient outcomes. The emergence of big data infrastructures, coupled with artificial intelligence (AI), offers a transformative opportunity to harness this information for safer, more efficient and better personalized transfusion practices. This narrative review outlines current and potential applications of AI and machine learning (ML) at each phase of the big data pipeline in transfusion medicine, including data collection, wrangling and harmonization, validation, feature engineering, analysis, publication and knowledge mobilization. We discuss how AI\u2010enabled methods\u2014such as natural language processing to extract variables, anomaly detection for product quality, supervised models to predict risks, federated analysis for collaboration,\u00a0and forecasting algorithms to optimize inventory and logistics\u2014may address longstanding challenges related to data fragmentation, unstructured documentation and labour\u2010intensive manual validation. We emphasize critical risks and limitations of applying AI to big data analytics and discuss mitigation through robust governance, performance monitoring, fairness audits, cybersecurity measures and transparent human oversight. We end by offering key recommendations and future directions, highlighting that strategic, equitable and ethically sound implementation will be essential to realizing benefits and ensuring trust in an increasingly data\u2010driven transfusion ecosystem.<\/jats:p>","DOI":"10.1111\/vox.70236","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T02:21:12Z","timestamp":1774232472000},"page":"441-452","source":"Crossref","is-referenced-by-count":6,"title":["Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency"],"prefix":"10.1111","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8247-1484","authenticated-orcid":false,"given":"Sheharyar","family":"Raza","sequence":"first","affiliation":[{"name":"Medical Affairs and Innovation Canadian Blood Services  Toronto Canada"},{"name":"Department of Biostatistics University of Toronto  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruchika","family":"Goel","sequence":"additional","affiliation":[{"name":"Simmons Cancer Institute, Division of Hematology\u2010Oncology, Department of Internal Medicine SIU School of Medicine  Springfield Illinois USA"},{"name":"Department of Pathology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Erikstrup","sequence":"additional","affiliation":[{"name":"Aarhus University  Aarhus Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-6490","authenticated-orcid":false,"given":"Angelo","family":"D'Alessandro","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Genetics University of Colorado Anschutz Medical Campus  Denver Colorado USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-366X","authenticated-orcid":false,"given":"Brian","family":"Custer","sequence":"additional","affiliation":[{"name":"Vitalant Research Institute  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Community Health Sciences, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Michael G. DeGroote Centre for Transfusion Research McMaster University  Hamilton Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the ISBT BIG DATA Working Party","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,22]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.18257"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.12468"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.12986"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)60631-5"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/tme.12223"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0217-0"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/vox.70145"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/vox.70182"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1515\/jib-2017-0030"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0727-5"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17507"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2014-10-605493"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.16939"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.17974"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.20121"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2025.150936"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1423-0410.1981.tb00691.x"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100727"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/17474086.2025.2610282"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-000812"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2025.111746"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.18013"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2352-3026(25)00068-7"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000116"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad247"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02363-5"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.2196\/69004"},{"key":"e_1_2_10_31_1","doi-asserted-by":"crossref","unstructured":"WangS McDermottMBA ChauhanG HughesMC NaumannT GhassemiM.MIMIC\u2010extract: a data extraction preprocessing and representation pipeline for MIMIC\u2010III.2019.https:\/\/doi.org\/10.48550\/ARXIV.1907.08322","DOI":"10.1145\/3368555.3384469"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2010.05.002"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3650203.3663334"},{"key":"e_1_2_10_35_1","unstructured":"FallahpourA MagnusonA GuptaP MaS NaimerJ ShahA et al.BioReason: incentivizing multimodal biological reasoning within a DNA\u2010LLM model [Internet]. arXiv. 2025 May 29. 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