Abstract Transfusion medicine generates enormous volumes of data across the vein‐to‐vein continuum, spanning donor characteristics, laboratory testing, component manufacturing, logistics and recipient outcomes. The emergence of big data infrastructures, coupled with artificial intelligence (AI), offers a transformative opportunity to harness this information for safer, more efficient and better personalized transfusion practices. This narrative review outlines current and potential applications of AI and machine learning (ML) at each phase of the big data pipeline in transfusion medicine, including data collection, wrangling and harmonization, validation, feature engineering, analysis, publication and knowledge mobilization. We discuss how AI‐enabled methods—such as natural language processing to extract variables, anomaly detection for product quality, supervised models to predict risks, federated analysis for collaboration, and forecasting algorithms to optimize inventory and logistics—may address longstanding challenges related to data fragmentation, unstructured documentation and labour‐intensive manual validation. We emphasize critical risks and limitations of applying AI to big data analytics and discuss mitigation through robust governance, performance monitoring, fairness audits, cybersecurity measures and transparent human oversight. We end by offering key recommendations and future directions, highlighting that strategic, equitable and ethically sound implementation will be essential to realizing benefits and ensuring trust in an increasingly data‐driven transfusion ecosystem.
Raza et al. (Sun,) studied this question.