Artificial Intelligence and Machine Learning in Transfusion Practice: An Analytical Assessment
Bibliographic record
Abstract
Transfusion medicine is vital to healthcare and affects clinical outcomes, patient safety, and system resilience while addressing challenges such as blood shortages, donor variability, and rising costs. The integration of artificial intelligence (AI) and machine learning (ML) presents new opportunities to improve clinical decision-making and operational effectiveness in this field. This structured narrative review identified and evaluated studies applying AI and ML in transfusion medicine. A search of PubMed and Scopus for articles published between January 2018 and April 2025 yielded 565 publications. Studies were included if they applied AI or ML techniques, focused on transfusion management or decision support, and were evaluated using electronic health records or expert review. Four exemplar studies were selected, each representing a distinct AI paradigm: supervised, unsupervised, reinforcement, and generative learning. These studies were critically appraised for methodological rigor, clinical relevance, and potential for implementation in practice. The reviewed studies reflected a clear shift from traditional analytic methods toward more advanced computational approaches to improve prediction accuracy, optimize resource allocation, and support clinical decision-making. Three overarching themes emerged: the need to balance model complexity with interpretability and clinical feasibility; the impact of data quality and preprocessing on model performance and fairness; and the barriers to broader applicability and cross-institutional deployment. As technological barriers continue to decline, future challenges will increasingly center on privacy regulations, infrastructure constraints, and aligning model complexity with practical utility. Thoughtful integration of these considerations through scalable, clinical-grade, and transparent solutions will be critical in realizing the full potential of AI and ML in transfusion medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".