Keyword: Transfusion Medicine

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.