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Correspondence to Author: Dr. Neha Singh. 

AIIMS (All India institute of Medical Sciences) Patna, Bihar, India

DOI: 10.52338/joct.2026.5361

Abstract:

Artificial intelligence (AI)—including machine learning (ML), deep learning (DL), and natural language processing (NLP)—is transforming transfusion medicine (TM) across the donor-to-recipient continuum. This review synthesizes applications in donor recruitment-motivation and mobilization, donor screening, laboratory microbiology and immunohematology, component quality assessment, inventory forecasting, clinical decision support and patient blood management (PBM), and hemovigilance. We summarize reported benefits, implementation pitfalls, regulatory and ethical considerations, and pragmatic steps to deploy AI safely. Across domains, AI shows promise to improve safety, reduce wastage, and personalize care; however, rigorous prospective evaluation, calibration, transparency, and human-in-the-loop governance remain essential.

Keywords: Transfusion medicine, artificial intelligence, machine learning, inventory forecasting, immunohematology, hemovigilance, patient blood management.

Citation:

Dr. Neha Singh, Artificial Intelligence In Transfusion Medicine: A Contemporary Review. Journal of Clinical Transplantation 2026.