人工智能在输血医学中的实施:应对临床采用的挑战
Suzanne Maynard1, Joseph Farrington2, Sheharyar Raza3
1Radcliffe Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK; NIHR Data Driven Transfusion Practice, Blood and Transplant Research Unit, Oxford, UK.
Transfusion medicine reviews
|February 22, 2026
概括
人工智能 (AI) 和机器学习 (ML) 工具对血液管理有希望,但面临实施挑战. 现实世界的研究揭示了像临床需求调整这样的推动因素,以及像数据质量这样的障碍,但尚未证明结果有所改善.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健服务研究 医疗服务研究
- 临床病理学 临床病理学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被认为具有改善输血和患者血液管理的潜力.
- 尽管有推广,但AI/ML在这些临床领域的现实世界整合仍然有限.
研究的目的:
- 对AI/ML在输血和患者血液管理中的部署进行示例性研究进行审查.
- 确定AI/ML整合到临床工作流程中的转化特征,障碍和推动因素.
主要方法:
- 我们对PubMed和Web of Science进行了系统的搜索,寻找从2022年1月起的文章.
- 选择了三项与工作流集成的前性研究,以对AI/ML实施进行详细分析.
主要成果:
- 其中包括用于费里丁预测的实验室工具,用于血红蛋白估计的患者应用程序,以及用于创伤复苏需求的临床医生工具.
- 共同的推动因素是临床需求的调整,现有基础设施,可解释的模型和利益相关者的参与.
- 主要障碍包括数据质量,概括性和缺乏经济评估;没有一项研究显示改善了临床结果或成本.
结论:
- 成功采用AI/ML需要无集成到常规工作流程中,并制定强有力的安全,监控和监管计划.
- 未来的研究应该优先考虑实施框架,评估下游影响,并专注于可扩展的解决方案,如实验室嵌入式分析和数字患者工具.
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