{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T21:33:11Z","timestamp":1790631191377,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"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:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) and big data are technologies with the potential to transform transfusion medicine (TM). This survey explored the scope of AI and big data use in TM across the Eastern Mediterranean and North Africa region.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A survey was distributed among transfusion professionals to explore current use, perceived benefits and barriers to adopting AI and big data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifty respondents participated; the majority worked in national\/regional transfusion services, and 58% worked in academic institutions. Only 24% reported using AI in daily TM practice, primarily for administrative tasks, education and research. Clinical applications were mainly in blood donor recruitment and management. Most used generative AI tools (92%) and were self\u2010taught. Big data were employed in 36% of respondents' institutions, most often for inventory forecasting and optimizing blood product utilization. Most institutions used data based on laboratory information systems (89%), donor databases (72%) and electronic healthcare\/patient records (67%). The main challenges and concerns regarding AI adoption were the lack of regulatory guidance, limited expertise, insufficient clinical validation of AI tools, implementation cost and ethical and privacy concerns. In terms of big data, the key barriers were insufficient expertise in data management and a lack of infrastructure for data storage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI and big data adoption in TM within the region remains limited. Major barriers include regulatory gaps, lack of expertise, cost constraints and infrastructure limitations. Strategic investment in regulatory frameworks, targeted training and technical resources is essential to facilitate safe and effective integration into transfusion practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vox.70145","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T02:00:39Z","timestamp":1762394439000},"page":"511-519","source":"Crossref","is-referenced-by-count":4,"title":["Use of artificial intelligence and big data in transfusion medicine: An exploratory assessment of status in the Eastern Mediterranean and North Africa region"],"prefix":"10.1111","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8649-0650","authenticated-orcid":false,"given":"Arwa Z.","family":"Al\u2010Riyami","sequence":"first","affiliation":[{"name":"Department of Haematology Sultan Qaboos University Hospital, University Medical City  Muscat Oman"},{"name":"College of Medicine and Health Sciences Sultan Qaboos University  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suha","family":"Herjes","sequence":"additional","affiliation":[{"name":"Royal Medical Services King Hamad University Hospital  Busaiteen Bahrain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8030360"},{"key":"e_1_2_9_3_1","unstructured":"Cole StrykerEK.What is Artificial Intelligence (AI)?2024IBM. Available from:https:\/\/www.ibm.com\/think\/topics\/artificial-intelligence. Last accessed 25 Aug 2025."},{"key":"e_1_2_9_4_1","unstructured":"Big Data Characteristics.Enterprise Big Data Framework.2025. Data Science Institute. Available from:https:\/\/www.bigdataframework.org\/knowledge\/big-data-characteristics\/. Last accessed 11 Aug 2025."},{"key":"e_1_2_9_5_1","unstructured":"Care CAfDaTiH.2025 Watch List: Artificial Intelligence in Health Care.2025. Available from:https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK613808\/. 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