ReviewActa haematologica2025
AI Applications in Transfusion Medicine: Opportunities, Challenges, and Future Directions.
Review in Acta haematologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.Annals of medicine · 2026Article
- Fundamentals of big data and artificial intelligence in transfusion medicine.Vox sanguinis · 2026Review
- AI-Driven Innovations in Transfusion Medicine: A Narrative Synthesis of Current Reviews.Medical sciences (Basel, Switzerland) · 2025Review
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
<p>Background: Artificial intelligence (AI) is reshaping healthcare, with its applications in transfusion medicine (TM) showing great promise to address longstanding challenges. Summary: This review explores the integration of AI-driven tools, including machine learning, deep learning, natural language processing, and predictive analytics, across various domains of TM. From enhancing donor management and optimizing blood product quality to predicting transfusion needs and assessing bleeding risks, AI has demonstrated its potential to improve operational efficiency, patient safety, and resource allocation. Additionally, AI-powered systems enable more accurate blood antigen phenotyping, automate hemovigilance workflows, and streamline inventory management through advanced forecasting models. While these advancements are largely exploratory, early studies highlight the growing importance of AI in improving patient outcomes and advancing precision medicine. However, challenges such as variability in clinical workflows, algorithmic transparency, equitable access, and ethical concerns around data privacy and bias must be addressed to ensure responsible integration. Key Messages: (i) AI-driven tools are being applied across multiple domains of TM. (ii) Early studies demonstrate the potential for AI to improve efficiency, safety, and personalization. (iii) Key implementation challenges include data privacy, workflow integration, and equitable access. </p>.
Indexed as
Identifiers
What OpenQuestion holds
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.