Predicting Transfusion Risks to Power Safer Blood Management

link.springer.com October 30, 2025

This systematic review analyzed the use of Artificial Intelligence (AI) models over the past decade in identifying and predicting Adverse Transfusion Reactions (ATRs) and their implications for clinical management.

The 24 included studies primarily applied AI models across four areas: transfusion risks and outcomes, risk and moderating factors, transfusion volume and intensity, and classification/extraction of ATRs.

The Random Forest (RF) model was the most frequently used AI approach.

The results suggest that individual patient factors and transfusion volume are pivotal in determining the occurrence of ATRs.

The review underscores the importance of integrating AI into clinical practice through Clinical Decision Support Systems (CDSS) and Electronic Health Records (EHR) to enable personalized and safer transfusion strategies. This must be done while strictly adhering to ethical considerations and patient privacy.