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Artificial intelligence and machine learning in immunohematology: an expert overview of emerging tools for precision transfusion medicine Cover

Artificial intelligence and machine learning in immunohematology: an expert overview of emerging tools for precision transfusion medicine

Paid access
|Oct 2026

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References

  1. Harrison DM, Mallory DM. Application of a software program for cell selection in the reference laboratory. Transfusion 1995; 35:75S.
  2. Rosales MA, de Luna RG. Computer-based blood type identification using image processing and machine learning algorithm. Journal of Advanced Computational Intelligence and Intelligent Informatics 2022;26:698–705.
  3. Larpant N, Niamsi W, Noiphung J, et al. Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation. Anal Chim Acta 2022;1207:339807.
  4. Moslemi C, Sækmose S, Larsen R, et al. A deep learning approach to prediction of blood group antigens from genomic data. Transfusion 2024;64:2179–95.
  5. Hyvärinen K, Haimila K, Moslemi C, et al. A machine-learning method for biobank-scale genetic prediction of blood group antigens. PLoS Comput Biol 2024;20:e1011977.
  6. Borgmann DM, Mayr S, Polin H, et al. Single molecule fluorescence microscopy and machine learning for Rhesus D antigen classification. Sci Rep 2016;6:32317.
  7. Wang L, Yang J, Guo Z, Qiu D, Fu Y, Xu Y. Development and validation of a risk prediction model and management strategy for red blood cell irregular antibodies a retrospective cohort study of 521 patients. Clin Exp Med 2026;26:217. doi:10.1007/s10238-026-02142-2.
  8. Bio-Rad IH-AbID. Available from https://www.bio-rad.com/en-in/product/ih-abid?ID=PU89TLWDLBV5. Accessed 11 November 2025.
  9. Transforming blood availability with Grifols Blood Typing Manager. Available from www.diagnostic.grifols.com/en/-/transforming-blood-availability-with-grifols-blood-typing-manager. Accessed 17 August 2026.
  10. Callaway D, Murphy-Wall B. Does in-house antibody identification testing with software assistance actually improve patient care and reduce hospital expenses? Transfusion 2025;65(Suppl 2):204A. doi:10.1111/trf.18417.
  11. Gleadall N. Genotype and machine learning based blood matching: towards improved transfusion outcomes. Reykjavík: University of Iceland; 2 April 2025. Available from https://lifvisindi.hi.is/genotype-and-machine-learning-based-blood-matching-towards-improved-transfusion-outcomes. Accessed 27 March 2026.
  12. Jensen EA, Serhatlioglu M, Uyanik C, et al. Label-free blood typing by Raman spectroscopy and artificial intelligence. Advanced Materials Technologies 2024;9:2301462.
  13. Moslemi C, Sækmose SG, Larsen R, et al. Genetic prediction of 33 blood group phenotypes using an existing genotype dataset. Transfusion 2023;63:2297–310.
  14. Wemelsfelder ML, van de Weem RHG, Luken JS, et al. Extensive red blood cell matching considering patient alloimmunization risk. Vox Sang 2024;119:368–76.
  15. Wolf J, Blais-Normandin I, Bathla A, et al. Red cell specifications for blood group matching in patients with haemoglobinopathies: an updated systematic review and clinical practice guideline from the International Collaboration for Transfusion Medicine Guidelines. Br J Haematol 2025;206:94–108. doi:10.1111/bjh.19837.
  16. U.S. Food and Drug Administration. Guidance for industry: “computer crossmatch” (computerized analysis of the compatibility between the donor’s cell type and the recipient’s serum or plasma type). Available from https://www.fda.gov/media/80857/download. Accessed 11 November 2025.
  17. Gleadall NS, Veldhuisen B, Gollub J, et al. Development and validation of a universal blood donor genotyping platform: a multinational prospective study. Blood Adv 2020;4:3495–506.
  18. Smit Sibinga CT. Artificial intelligence and the future of transfusion medicine. Neurosci Chron 2021;2:25–30.
  19. Barzilai M, Cohen O. AI applications in transfusion medicine: opportunities, challenges, and future directions. Acta Haematol 2025;148:516–26.
DOI: https://doi.org/10.2478/immunohematology-2026-014 | Journal eISSN: 1930-3955 | Journal ISSN: 0894-203X
Language: English
Page range: 82 - 85
Published online: Oct 11, 2026
Published in print: Sep 1, 2026
Published by: American National Red Cross
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2026 Soumya Das, Rounak Dubey, Parag Fulzele, published by American National Red Cross
This work is licensed under the Creative Commons License.