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June 14, 2026Transfusion Medicine and HemotherapyOpen Access

AI and Blood Banking: Predicting Transfusion Demand - A Systematic Review of Forecasting Approaches

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Authors

MEMerlin EngelkeEssen University HospitalOÇOsman Alperen Çinar-KoraşJKJens Kleesiek

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Implication

Systematic review highlights AI's efficacy in predicting blood product demand, indicating improved supply management.

Key Points

  • This review aims to evaluate the effectiveness of AI and ML in predicting blood product demand for efficient supply management.
  • Conducted a systematic review adhering to PRISMA 2020 guidelines.
  • Searched PubMed and Scopus for studies from January 2015 to December 2025.
  • Organized findings in a four-tier framework based on prediction scope and clinical goal.
  • 24 studies met inclusion criteria from an initial 535 records identified.
  • Individual prediction models achieved AUCs of 0.45–0.97 for short-term predictions.
  • Facility-level optimization reduced platelet outdating rates by approximately 50% with MAPEs as low as 4.18%.

Cite This Study

Engelke et al. (2026) studied this question.

synapsesocial.com/papers/6a2e482cb1cc60ccdea8c85ehttps://doi.org/10.1159/000552969
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Also Consider

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  1. 1Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications2025 · 6 citations
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  4. 4Role of Artificial Intelligence in Enhancing the Efficiency of Transfusions in Neonatal and Pediatric Patients2026
  5. 5Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency2026 · 2 citations