Integrating AI and Multi-Omics for Predicting Transfusion Reactions: A Step Toward Safe and Personalized Transfusion Medicine

Abstract

Background: Blood transfusion is a lifesaving process, but the risk of adverse reactions ranges from mild to life-threatening. Current detection methods with limited sensitivity lack predictive power.

Aim: To explore how artificial intelligence with multi-omics data such as genomics, transcriptomics, proteomics, and metabolomics enhances early prediction of transfusion reactions. This study aims to highlight recent advances, AI methodologies, and challenges to implementing these technologies.

Methods: This study is a narrative review in which articles from PubMed, Wiley Online Library, Science Direct, MDPI portfolio, Frontiers journals and Google Scholar databases using filter year 2021 to 2025 were searched and included.

Results: AI with multi-omics data has a transformative potential for early, personalized, and accurate prediction of transfusion-related complications. Multi-modal fusion strategies (early, intermediate, late) also increase prediction power by combining different omics layers.

Conclusion: Challenges are also present in this technology including data heterogeneity, ethical concerns, and limited real-time data from different populations. However, the future of this technology with electronic health records of hospitals, and continued validations, will revolutionize transfusion medicine by minimizing transfusion related risks.