Fundamentals of big data and artificial intelligence in transfusion medicine

Abstract Within the digital transformation of medicine, transfusion medicine has quietly become a big‐data discipline. The long‐standing tradition of blood product standardization (e.g., ISBT‐128) and large donor cohorts being followed over years—some of which are sampled in national biobank projects, build a favourable setting. In parallel, recent advances in artificial intelligence (AI) and data integration facilitate efficient data use for research and clinical care. Consequently, next‐generation blood services might monitor donor phenotype data and match this information to AI‐predicted recipient demands and their outcomes. Here, we attempt to provide a comprehensive introduction to the possibilities and challenges of big data and AI in transfusion medicine along with data integration opportunities related to the Fast Healthcare Interoperability Resources standard. We educate on the principles of AI and the digital transformation of transfusion medicine and analyse the evidence of blood establishments as digital platforms. We illustrate possible roadmaps for data integration and how federated learning initiatives and national networks may scale value while preserving donor and patient privacy. Finally, we exemplify the ongoing transformation with precision red blood cell (RBC) diagnostics using lab‐on‐a‐chip and the digital crossmatch. The practice of transfusion medicine is undergoing transformation and experimentally appears to profit from synergies in precision diagnostics and AI. Its translation into routine practice remains a challenge for the current decade to leverage the full potential of blood establishments as ‘big‐data engines’.