Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems

Abstract

Background: Blood transfusion is essential for patient safety, yet traditional hemovigilance systems face challenges including underreporting, data integration issues, and slow response times. Artificial intelligence (AI) offers solutions through advanced information systems capable of identifying, analyzing, and reporting transfusion events. Objective: This narrative review examines the potential of emerging AI technologies to enhance hemovigilance, focusing on data integration, adverse event detection, personalized risk management, and blood supply chain optimization. Methods: A comprehensive literature review was conducted using PubMed, Scopus, IEEE Xplore, Web of Science, Google Scholar, and Embase, covering studies from 2010 to 2024. AI applications, including machine learning, deep learning, natural language processing (NLP), and predictive analytics, were analyzed for their impact on transfusion safety and operational efficiency. Findings: AI improves real-time data acquisition, detection of transfusion-related adverse events, predictive risk assessment, and supply chain management through demand forecasting and waste reduction. NLP facilitates integration of unstructured clinical data, while AI-driven decision support systems enable proactive and personalized patient care. Limitations: Challenges include data privacy, algorithmic bias, regulatory gaps, and dependency on data quality. Future directions involve federated learning, explainable AI, and standardization to ensure secure, transparent, and equitable AI-based hemovigilance. Conclusion: AI has the potential to revolutionize hemovigilance, improving patient safety and efficiency, while addressing operational and ethical challenges.

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