ReviewVox sanguinis2026
Fundamentals of big data and artificial intelligence in transfusion medicine.
Review in Vox sanguinis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Precision transfusion medicine in the omics era.Blood · 2026Review
- Fundamentals of big data and artificial intelligence in transfusion medicine.Vox sanguinis · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Within the digital transformation of medicine, transfusion medicine has quietly become a big-data discipline. The long-standing tradition of blood product standardization (e.g., ISBT-128) and large donor cohorts being followed over years-some of which are sampled in national biobank projects, build a favourable setting. In parallel, recent advances in artificial intelligence (AI) and data integration facilitate efficient data use for research and clinical care. Consequently, next-generation blood services might monitor donor phenotype data and match this information to AI-predicted recipient demands and their outcomes. Here, we attempt to provide a comprehensive introduction to the possibilities and challenges of big data and AI in transfusion medicine along with data integration opportunities related to the Fast Healthcare Interoperability Resources standard. We educate on the principles of AI and the digital transformation of transfusion medicine and analyse the evidence of blood establishments as digital platforms. We illustrate possible roadmaps for data integration and how federated learning initiatives and national networks may scale value while preserving donor and patient privacy. Finally, we exemplify the ongoing transformation with precision red blood cell (RBC) diagnostics using lab-on-a-chip and the digital crossmatch. The practice of transfusion medicine is undergoing transformation and experimentally appears to profit from synergies in precision diagnostics and AI. Its translation into routine practice remains a challenge for the current decade to leverage the full potential of blood establishments as 'big-data engines'.
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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.