EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Dec 01, 2025 1206 196

Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges

Ikemefula Oriaku 1 * ,
Oluchi Okechukwu 2,
Obiageri Ihuarulam Okeoma 3,
Chidinma Gab-Obinna 4,
Jazuli Isyaku Bala 5,
Adeyinka Moyinoluwa Adejumobi 6,
Oluwabusayo Olufunke Awoyomi 7
1 Department of Medical Laboratory Science, University of Nigeria, Enugu Campus, Nigeria2 Department of Laboratory Medicine, University of Vermont Medical Center, Burlington, VT, USA3 Department of Medical Laboratory Science, Trinity University Yaba, Yaba, Lagos, Nigeria4 Biomedical Laboratory Science Department, Kingston University London, London, United Kingdom5 Department of Hematology and Transfusion Medicine, Faculty of Clinical Sciences, Bayero University Kano, Aminu Kano Teaching Hospital, Kano, Nigeria6 Department of Data Science, Artificial Intelligence and Modelling, University of Hull, Hull, United Kingdom7 Department of Computer Science, University of Albany, Albany, NY, USA* Corresponding Author
Epidemiology and Health Data Insights, 1(6), 2025, ehdi023, https://doi.org/10.63946/ehdi/17470
Publication date: Dec 01, 2025
Full Text (PDF)

ABSTRACT

Artificial intelligence (AI) is increasingly shaping modern healthcare by enabling data-driven decision-making, improving diagnostic accuracy, and optimizing resource use. In transfusion medicine, AI offers substantial opportunities to enhance donor management, automate blood typing and compatibility testing, strengthen inventory forecasting, and support early detection of transfusion-related complications. This review summarizes current applications of AI technologies—including machine learning, deep learning, natural language processing, computer vision, and predictive analytics—and evaluates their impact across laboratory, clinical, and operational domains. Emerging innovations such as precision transfusion, patient digital twins, multi-omics integration, and federated learning highlight AI’s potential to advance personalized and interconnected transfusion practices. However, successful implementation requires addressing challenges related to data heterogeneity, algorithmic bias, privacy and ethical considerations, and evolving regulatory requirements. Establishing rigorous validation standards and promoting interdisciplinary collaboration will be essential to ensure that AI improves the safety, efficiency, and sustainability of transfusion medicine.

KEYWORDS

Artificial Intelligence Machine Learning Transfusion Medicine Blood Safety Predic-tive Analytics

CITATION (Vancouver)

Oriaku I, Okechukwu O, Okeoma OI, Gab-Obinna C, Bala JI, Adejumobi AM, et al. Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges. Epidemiology and Health Data Insights. 2025;1(6):ehdi023. https://doi.org/10.63946/ehdi/17470
APA
Oriaku, I., Okechukwu, O., Okeoma, O. I., Gab-Obinna, C., Bala, J. I., Adejumobi, A. M., & Awoyomi, O. O. (2025). Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges. Epidemiology and Health Data Insights, 1(6), ehdi023. https://doi.org/10.63946/ehdi/17470
Harvard
Oriaku, I., Okechukwu, O., Okeoma, O. I., Gab-Obinna, C., Bala, J. I., Adejumobi, A. M., and Awoyomi, O. O. (2025). Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges. Epidemiology and Health Data Insights, 1(6), ehdi023. https://doi.org/10.63946/ehdi/17470
AMA
Oriaku I, Okechukwu O, Okeoma OI, et al. Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges. Epidemiology and Health Data Insights. 2025;1(6), ehdi023. https://doi.org/10.63946/ehdi/17470
Chicago
Oriaku, Ikemefula, Oluchi Okechukwu, Obiageri Ihuarulam Okeoma, Chidinma Gab-Obinna, Jazuli Isyaku Bala, Adeyinka Moyinoluwa Adejumobi, and Oluwabusayo Olufunke Awoyomi. "Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges". Epidemiology and Health Data Insights 2025 1 no. 6 (2025): ehdi023. https://doi.org/10.63946/ehdi/17470
MLA
Oriaku, Ikemefula et al. "Artificial Intelligence in Transfusion Medicine: Current Applications, Opportunities, and Challenges". Epidemiology and Health Data Insights, vol. 1, no. 6, 2025, ehdi023. https://doi.org/10.63946/ehdi/17470

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