Barzilai Merav, Cohen Omri
Blood Services and Apheresis Institute, Rabin Medical Center and Tel Aviv University, Tel Aviv, Israel.
Department of Transfusion Medicine, Kaplan Medical Center and The Hebrew University of Jerusalem, Jerusalem, Israel.
Acta Haematol. 2025 May 9:1-11. doi: 10.1159/000546303.
Artificial intelligence (AI) is reshaping healthcare, with its applications in transfusion medicine (TM) showing great promise to address longstanding challenges.
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.
(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.
人工智能（AI）正在重塑医疗保健领域，其在输血医学（TM）中的应用有望解决长期存在的挑战。
本综述探讨了人工智能驱动的工具，包括机器学习、深度学习、自然语言处理和预测分析，在输血医学各个领域的整合情况。从加强献血者管理、优化血液制品质量到预测输血需求和评估出血风险，人工智能已展现出提高运营效率、患者安全和资源分配的潜力。此外，人工智能驱动的系统能够实现更准确的血型抗原表型分析，通过先进的预测模型使血液警戒工作流程自动化，并简化库存管理。虽然这些进展大多仍处于探索阶段，但早期研究凸显了人工智能在改善患者预后和推进精准医学方面日益重要的作用。然而，必须应对诸如临床工作流程的变异性、算法透明度、公平获取以及围绕数据隐私和偏差的伦理问题等挑战，以确保负责任的整合。
（i）人工智能驱动的工具正在应用于输血医学的多个领域。（ii）早期研究证明了人工智能提高效率、安全性和个性化的潜力。（iii）关键的实施挑战包括数据隐私、工作流程整合和公平获取。
