Machine Learning for Donor Safety and Engagement: An Analytical Assessment

Blood donor recruitment, retention, and safety face mounting challenges from an aging donor base and declining donation rates, and there is need for innovation in donor engagement. Machine learning (ML) offers tools to model donor behavior, physiology, and risk, but the maturity, methodological rigor, and translational readiness of this literature have not been systematically characterized. This study aimed to systematically identify and critically appraise studies applying ML methods to blood donation. We conducted structured searches of PubMed for articles published since January 1, 2018 (search date May 7, 2026). Studies were included if they explicitly applied ML; addressed donor safety, engagement, recruitment, retention, health, or adverse reactions; and demonstrated empirical or real-world relevance. Included studies underwent critical appraisal of design, predictors, outcome definition, model development, validation strategy, and practical implications. Of 54 articles identified in systematic search, 5 were selected, addressing donor return prediction, lipemic plasma triglyceride screening in a small, highly selected cohort, anticipatory vasovagal reaction prediction from facial thermal imaging using neural networks, cross-country hemoglobin deferral prediction with model exchange, and externally validated iron recovery prediction using ensemble models. Gradient-boosted and tree-based ensemble models predominated, with SHAP-based interpretation, cross-validation, and external or multi-national validation being used in studies. Common limitations included small or highly preselected samples limiting generalizability; a lack of external validation; underuse of decision-analytic or hard operational endpoints; and minimal discussion of deployment, workflow integration, or ongoing evaluation. ML applications in blood donor services remain at an early, exploratory stage with promising but heterogeneous performance.