Artificial intelligence in blood transfusion management: Global reduction of blood component waste and the emergence of predictive models that save millions of lives (Research, Society and Development, 2025)
Blood transfusion management faces persistent challenges related to demand variability, the risk of shortages, and high levels of blood component waste, especially platelets. Artificial intelligence models have been highlighted for predicting transfusion patterns and optimizing stocks, offering more precise responses to healthcare systems. This study aims to investigate how artificial intelligence is being applied to blood component management, with an emphasis on two main axes: (1) the overall reduction of blood component waste and (2) the emergence of predictive models that effectively save lives by optimizing the availability, logistics, and use of these products. More specifically, it intends to review the literature from 2021 to 2025, identify practices, technologies, results, and gaps, and provide recommendations for the efficient and ethical adoption of these solutions in the transfusion healthcare sector. This is a hybrid systematic review, conducted according to PRISMA 2020, which analyzed 12 international studies applying AI, machine learning, deep learning, or optimization models to blood transfusion management. The results show that such models exhibit high accuracy in demand forecasting and contribute to more efficient inventory adjustments, loss reduction, and logistical streamlining. Multicenter evidence also points to increased availability of blood components and reduced operational costs. It is concluded that AI constitutes a strategic tool for improving transfusion efficiency and safety, although challenges such as interoperability, data governance, and large-scale validations still need to be overcome.