ReviewDigital health
Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.