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人工智能驱动的器官分配:对临床结果预测,伦理框架和决策算法进行系统审查
Faezeh Firuzpour1, Abazar Akbarzadeh Pasha2,3, Farshid Oliaei2,3
1Research Committee, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran.
BMC nephrology
|November 14, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在改善脏分配和预测移植存活方面表现有前途. 然而,现实世界的验证和伦理整合对于临床应用至关重要.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 移植是末期病的最佳治疗方法,但器官短缺和分配问题仍然存在.
- 人工智能 (AI) 和机器学习 (ML) 为改善结果和捐赠者与接受者匹配提供了潜在的解决方案.
- 目前AI/ML在脏分配中的临床整合受到验证和道德挑战的限制.
研究的目的:
- 系统地审查人工智能驱动的脏分配研究.
- 专注于预测建模,操作算法和道德考虑.
- 识别缺口并指导人工智能对脏分配的未来研究.
主要方法:
- 在生物医学,人工智能和临床试验数据库中进行全面的文学搜索.
- 包括16项研究 (回顾性,模拟,算法框架).
- 使用修改后的 QUADAS-2 和 PROBAST 工具进行质量评估;提取有关绩效,结果和公平性的数据.
主要成果:
- 人工智能/ML模型,特别是深度学习和整体方法,在预测移植存活率和等待列表结果方面表现优于传统得分 (C指数:0.65-0.72).
- 很少有研究将AI预测整合到实际的分配政策中,大多数仍在模拟中.
- 公平性和透明度等道德框架被不一致地应用,很少被算法嵌入.
结论:
- AI/ML具有显著的潜力,可以增强脏分配系统.
- 严格的临床翻译,现实世界的验证和道德治理至关重要.
- 需要多学科的合作来弥合预测准确性和公平实施之间的差距.
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