Authors
Soumya Das, Rounak Dubey, Parag Fulzele
Published in
Immunohematology. Volume 42. Issue 3. Pages 82-85. Sep 01, 2026. Epub Oct 11, 2026.
Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming immunohematology, offering significant enhancements in precision, accuracy, and personalized transfusion care. This expert overview summarizes current applications of AI/ML in phenotypic and genotypic red blood cell (RBC) antigen determination, antibody detection, crossmatching, and quality control. Recent studies demonstrate the efficacy of image-based and deep learning methods, with accuracies reaching 99 percent for automated phenotyping and genotyping. AI-assisted models for antibody detection and compatibility assessment have also emerged, providing rapid, standardized decision-making that reduces human error and improves transf usion outcomes. Notably, AI-driven platforms like the Haem-Match Consortium's bloodMatcher potentially reduce alloimmunization risks by as much as 80 percent, highlighting the technology's clinical relevance. AI also bolsters hemovigilance and quality control by analyzing vast clinical data for early detection of adverse events. Challenges remain, however, including algorithmic bias, data privacy, and the need for extensive real-world validation to ensure safe and ethical adoption. Integration with existing hospital information systems and development of robust regulatory frameworks are crucial to fully harness these tools. Future research should prioritize validation in diverse populations and transparent AI development practices. Overall, AI and ML hold promise to revolutionize immunohematology by enhancing accuracy, minimizing adverse transfusion reactions, and supporting patient-centric transfusion care.
PMID:
42859974
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.
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