{"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":1790631191456,"version":"4.1.0"},"reference-count":45,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Blood transfusion is essential for patient safety, yet traditional hemovigilance systems face challenges including underreporting, data integration issues, and slow response times. Artificial intelligence (AI) offers solutions through advanced information systems capable of identifying, analyzing, and reporting transfusion events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This narrative review examines the potential of emerging AI technologies to enhance hemovigilance, focusing on data integration, adverse event detection, personalized risk management, and blood supply chain optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A comprehensive literature review was conducted using PubMed, Scopus, IEEE Xplore, Web of Science, Google Scholar, and Embase, covering studies from 2010 to 2024. AI applications, including machine learning, deep learning, natural language processing (NLP), and predictive analytics, were analyzed for their impact on transfusion safety and operational efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>AI improves real-time data acquisition, detection of transfusion-related adverse events, predictive risk assessment, and supply chain management through demand forecasting and waste reduction. NLP facilitates integration of unstructured clinical data, while AI-driven decision support systems enable proactive and personalized patient care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Limitations<\/jats:title>\n                    <jats:p>Challenges include data privacy, algorithmic bias, regulatory gaps, and dependency on data quality. Future directions involve federated learning, explainable AI, and standardization to ensure secure, transparent, and equitable AI-based hemovigilance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI has the potential to revolutionize hemovigilance, improving patient safety and efficiency, while addressing operational and ethical challenges.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251406306","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:56:46Z","timestamp":1765205806000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7795-8892","authenticated-orcid":false,"given":"Md Faiazul","family":"Haque Lamem","sequence":"first","affiliation":[{"name":"R. P. Shaha University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muaj Ibne","family":"Sahid","sequence":"additional","affiliation":[{"name":"R. P. Shaha University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tracli.2022.09.070"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/trf.12980"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2014.08.005"},{"key":"e_1_3_3_5_2","first-page":"85","article-title":"Artificial intelligence in tuberculosis diagnosis: revolutionizing detection and treatment","volume":"9","author":"Yadav S","year":"2024","unstructured":"Yadav S, Jeyaraman N, Jeyaraman M, et al. Artificial intelligence in tuberculosis diagnosis: revolutionizing detection and treatment. IP Indian J Immunol Respir Med 2024; 9: 85\u201387.","journal-title":"IP Indian J Immunol Respir Med"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/jalm\/jfab075"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2022.100511"},{"key":"e_1_3_3_8_2","article-title":"The power of artificial intelligence for improved patient outcomes, ethical practices and overcoming challenges","volume":"2","author":"Almasri IA","year":"2024","unstructured":"Almasri IA. The power of artificial intelligence for improved patient outcomes, ethical practices and overcoming challenges. Medicine (Baltimore) 2024; 2.","journal-title":"Medicine (Baltimore)"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0208-8"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2006.05.002"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1159\/000530458"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2021.106183"},{"key":"e_1_3_3_13_2","first-page":"3006","article-title":"AI in healthcare: transforming patient care through predictive analytics and decision support systems","volume":"1","author":"Rana MS","year":"2024","unstructured":"Rana MS, Shuford J. AI in healthcare: transforming patient care through predictive analytics and decision support systems. J Artif Intell Gen Sci (JAIGS) 2024; 1: 3006\u20134023.","journal-title":"J Artif Intell Gen Sci (JAIGS)"},{"key":"e_1_3_3_14_2","first-page":"31","article-title":"Natural language processing advancements: breaking barriers in human\u2013computer interaction","volume":"3","author":"Ram\u00edrez JG","year":"2024","unstructured":"Ram\u00edrez JG. Natural language processing advancements: breaking barriers in human\u2013computer interaction. J Artif Intell General Sci (JAIGS) 2024; 3: 31\u201339.","journal-title":"J Artif Intell General Sci (JAIGS)"},{"key":"e_1_3_3_15_2","first-page":"6","article-title":"Artificial intelligence and machine learning for hemorrhagic trauma care","volume":"10","author":"Peng HT","year":"2023","unstructured":"Peng HT, Siddiqui MM, Rhind SG, et al. Artificial intelligence and machine learning for hemorrhagic trauma care. Mil Med Res 2023; 10: 6.","journal-title":"Mil Med Res"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/info14010031"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.51594\/ijmer.v6i3.882"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.orhc.2021.100290"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","unstructured":"Sadri S Shahzad A Zhang K. Blockchain traceability in healthcare: blood donation supply chain. In: 2021 23rd International conference on advanced communication technology (ICACT) February 7 2021 pp. 119\u2013126. IEEE.","DOI":"10.23919\/ICACT51234.2021.9370704"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksus.2024.103409"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13595-020-01011-6"},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","unstructured":"Bhatnagar A Shukla S Majumdar N. Machine learning techniques to reduce error in the internet of things. In: 2019 9th International conference on cloud computing data science & engineering (confluence) January 10 2019 pp. 403\u2013408. IEEE.","DOI":"10.1109\/CONFLUENCE.2019.8776619"},{"key":"e_1_3_3_23_2","first-page":"198","article-title":"Detecting cybersecurity attacks in internet of things using artificial intelligence methods: a systematic literature review","volume":"11","author":"Abdullahi M","year":"2022","unstructured":"Abdullahi M, Baashar Y, Alhussian H, et al. Detecting cybersecurity attacks in internet of things using artificial intelligence methods: a systematic literature review. Electronics (Basel) 2022; 11: 198.","journal-title":"Electronics (Basel)"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70341-6"},{"key":"e_1_3_3_25_2","doi-asserted-by":"crossref","unstructured":"Nunavath V Goodwin M. The use of artificial intelligence in disaster management-a systematic literature review. In: 2019 International conference on information and communication technologies for disaster management (ICT-DM) December 18 2019 pp. 1\u20138. IEEE.","DOI":"10.1109\/ICT-DM47966.2019.9032935"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.glmedi.2024.100132"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infoh.2024.05.003"},{"key":"e_1_3_3_28_2","article-title":"Artificial intelligence in healthcare delivery: prospects and pitfalls","volume":"16","author":"Olawade DB","year":"2024","unstructured":"Olawade DB, David-Olawade AC, Wada OZ, et al. Artificial intelligence in healthcare delivery: prospects and pitfalls. J Med Surgery Public Health 2024; 16:100108.","journal-title":"J Med Surgery Public Health"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100148"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-023-04698-z"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare12040481"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11040337"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.2196\/40781"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.21037\/aob-22-2"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-020-01299-w"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.62304\/ijse.v1i04.188"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110273"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109246"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109370"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.glmedi.2024.100099"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi15110370"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.59022\/ijlp.203"},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","unstructured":"Long G Shen T Tan Y et al. Federated learning for privacy-preserving open innovation future on digital health. In: Humanity driven AI: productivity well-being sustainability and partnership December 2 2021 pp. 113\u2013133. Cham: Springer International Publishing.","DOI":"10.1007\/978-3-030-72188-6_6"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.103663"},{"key":"e_1_3_3_45_2","article-title":"Trigger tool-based automated adverse event detection in electronic health records","volume":"20","author":"Musy SN","year":"2018","unstructured":"Musy SN, Ausserhofer D, Schwendimann R, et al. Trigger tool-based automated adverse event detection in electronic health records. Systemat Rev J Med Internet Res 2018; 20: e198.","journal-title":"Systemat Rev J Med Internet Res"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1159\/000530458"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251406306","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076251406306","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251406306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:55:24Z","timestamp":1777398924000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076251406306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":45,"alternative-id":["10.1177\/20552076251406306"],"URL":"https:\/\/doi.org\/10.1177\/20552076251406306","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]},"article-number":"20552076251406306"}}