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Related Concept Videos

Blood Transfusion01:15

Blood Transfusion

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Blood transfusion is a critical medical procedure that saves lives and treats various medical conditions. It involves transferring blood from a donor to a recipient. This process requires a thorough understanding of the ABO blood group system and its associated antigens and antibodies.
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Blood transfusion is a therapeutic measure to restore the blood volume after extensive blood loss due to an accident or a medical procedure. Blood transfusion involves drawing a certain amount of blood from a suitable donor and infusing it into the recipient.
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Artificial Intelligence and Machine Learning in Transfusion Practice: An Analytical Assessment.

Na Li1, Ruchika Goel2, Sheharyar Raza3

  • 1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Alberta, Canada; Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.

Transfusion Medicine Reviews
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) show promise for improving transfusion medicine, enhancing decision-making and operational efficiency. Key challenges involve data quality, model interpretability, and practical implementation for widespread clinical use.

Keywords:
Artificial intelligenceClinical decision supportGenerative artificial intelligenceReinforcement learningSupervised learningTransfusion medicineUnsupervised learning

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Area of Science:

  • Transfusion Medicine
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transfusion medicine faces challenges like blood shortages and rising costs.
  • Artificial intelligence (AI) and machine learning (ML) offer potential solutions for improving healthcare outcomes and system resilience.

Purpose of the Study:

  • To review and evaluate studies applying AI and ML in transfusion medicine.
  • To identify trends, challenges, and future directions for AI/ML integration in this field.

Main Methods:

  • A structured narrative review of 565 publications from PubMed and Scopus (Jan 2018 - Apr 2025).
  • Inclusion criteria focused on AI/ML techniques in transfusion management and decision support, evaluated via electronic health records or expert review.
  • Four exemplar studies representing supervised, unsupervised, reinforcement, and generative learning were critically appraised.

Main Results:

  • A shift towards advanced computational approaches for prediction accuracy and resource optimization.
  • Identified themes include balancing model complexity with interpretability, data quality impact, and implementation barriers.
  • Studies demonstrated AI/ML's potential to support clinical decision-making.

Conclusions:

  • Realizing AI/ML's full potential in transfusion medicine requires addressing privacy, infrastructure, and practical utility.
  • Scalable, clinical-grade, and transparent solutions are crucial for successful integration.
  • Future efforts must focus on overcoming implementation barriers for broader adoption.