The transformative power of artificial intelligence and machine learning in blood banking/transfusion medicine: A vein-to-vein approach from blood collection to transfusion services: On behalf of the AABB Foundation.

Abstract

Background: Artificial intelligence (AI)/machine learning (ML) are poised to transform transfusion medicine across the vein-to-vein continuum, from donor recruitment to recipient outcomes. Large datasets generated from millions of donations and transfusions create opportunities for predictive modeling, precision medicine, improved safety, and operational efficiency. This review outlines key applications of AI in blood banking and transfusion medicine using a comprehensive vein-to-vein framework.

Methods: The review synthesizes perspectives from blood services and hospital-based transfusion practices. Applications are discussed across donor recruitment, collections, testing, inventory management, hospital transfusion services, and education. Finally, AI safety, including ethical, regulatory, privacy, and implementation challenges, is addressed. This is a narrative, non-exhaustive review; given the rapidly evolving pace of AI/ML capabilities, this synthesis should be interpreted as a framework for ongoing surveillance rather than a fixed inventory of applications.

Results: Donor-focused applications include personalized recruitment, individualized iron management, and fairness auditing to reduce inequities. In supply chain operations, ML can improve demand forecasting, minimize waste, and optimize inventory. Federated learning may support multi-institutional collaboration without sharing raw data. Omics and genomic applications can enable high-throughput analysis, phenotype prediction, and precision transfusion support for rare blood types and alloimmunized patients. In hospitals, AI can augment hemovigilance by identifying/predicting adverse transfusion reactions, while large language models and retrieval-augmented generation may enhance decision support, patient blood management, and education through adaptive learning.

Conclusion: AI holds substantial promise to improve transfusion safety, efficiency, equity, and sustainability. Realizing this potential will require robust governance, model validation across diverse populations, privacy safeguards, bias mitigation, reproducibility, regulatory oversight, and human-in-the-loop implementation.