From prediction to practice: Barriers to implementing artificial intelligence in blood inventory management and transfusion support.

BACKGROUND

Artificial intelligence (AI) is increasingly applied to blood-demand forecasting, donor management, inventory optimisation, wastage reduction and transfusion-related decision support; however, routine implementation remains limited.

OBJECTIVES

To review current applications of AI in blood inventory management and transfusion support and identify the principal barriers to safe and sustainable implementation.

METHODS

A focused narrative review informed by structured searches of PubMed, Scopus, EBSCO and Google Scholar, supplemented by targeted searches of transfusion-specific and healthcare AI implementation literature. Evidence was synthesised thematically.

RESULTS

Published studies demonstrate technical feasibility across forecasting, donor-return prediction, inventory management and clinical decision support. Major implementation barriers include limited external and prospective validation, incomplete reporting, data-access and privacy constraints, poor interoperability, limited explainability, workforce training gaps and unclear governance.

CONCLUSIONS

The principal challenge is no longer developing predictive models but integrating them safely and sustainably into routine transfusion services. Future progress requires multicentre evaluation, privacy-preserving data collaboration, workflow-integrated design, role-specific training and explicit governance with continued human oversight.