AI-Driven Innovations in Transfusion Medicine: A Narrative Synthesis of Current Reviews

Background: Recent advancements in blood transfusion and transfusion medicine have increasingly integrated innovative technologies, including artificial intelligence (AI), machine learning, and computational intelligence. Despite numerous reviews on these topics, a comprehensive synthesis of the existing evidence is lacking. Objective: This narrative review of reviews aims to summarize and critically appraise the current literature on AI-driven and emerging technological approaches in blood transfusion, providing a structured overview for researchers and clinicians. Methods: A total of 19 reviews were selected through a systematic search strategy. Studies were assessed for methodological quality, scope, and clinical relevance, using adapted criteria from narrative review checklists. Data were extracted regarding the type of technology, application in transfusion medicine, study population, and reported outcomes. Results: The included reviews highlight several key domains: AI-assisted prediction of transfusion requirements, automated blood typing and crossmatching, advanced monitoring of blood products, and integration of computational models in blood banking workflows. Most studies reported promising applications but revealed substantial heterogeneity in methods, limited clinical validation, and variable reporting quality. Conclusions: AI and emerging technologies offer significant potential to improve the safety, efficiency, and personalization of blood transfusion. However, standardization of study designs, comprehensive validation, and robust reporting are essential to translate these innovations into routine clinical practice. This review of reviews provides a structured synthesis to guide future research and implementation strategies in transfusion medicine.