(untitled) (Research, Society and Development, 2026)
The adequate availability of blood components is a critical challenge for healthcare systems, especially given the variability in healthcare demand and the operational limitations of blood transfusion services. The objective of this study was to apply artificial intelligence techniques to predict the criticality of blood transfusion stocks, based on the analysis of operational data from a Brazilian public blood center, from January to March 2026. This is an observational, retrospective, and analytical study with a quantitative approach, using descriptive statistics and supervised machine learning models, including Random Forest and XGBoost. The results showed structured patterns of criticality, with greater stability in red blood cell concentrates and greater variability in platelet concentrates, which presented a more frequent recurrence of critical and emergency states. The models demonstrated satisfactory performance in predicting criticality levels, with high agreement between observed and predicted values. It is concluded that the application of artificial intelligence represents a promising tool for anticipating critical scenarios, optimizing the management of blood stocks, and supporting decision-making, contributing to transfusion safety and the efficiency of health services.