在过去十年中,人工智能在脏移植中的应用:对临床应用,当前的局限性和未来方向的叙述性审查
Ahmed Anber1,2, Youssef Mohamed3, Aryan Maleki3
1Urology, Barking Havering and Redbridge NHS Trust, London, GBR.
Cureus
|January 27, 2026
概括
人工智能 (AI) 和机器学习 (ML) 正在通过改善捐赠者-接受者匹配和预测结果来彻底改变移植 (KT). 虽然有前途,但数据偏差等挑战需要解决,以便更广泛的临床使用.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 移植手术 移植手术
- 医疗信息学 医疗信息学
背景情况:
- 移植 (KT) 仍然是末期病的关键治疗方法.
- 传统的捐赠者-接受者匹配和结果预测方法有局限性.
- 人工智能 (AI) 和机器学习 (ML) 的进步为KT提供了新的可能性.
研究的目的:
- 在过去十年中,审查AI和ML在移植中的应用.
- 突出移植前,手术前和移植后阶段的进展.
- 讨论AI/ML在KT中的未来潜力和当前局限性.
主要方法:
- 关于人工智能和移植中的ML文献的叙述性综述.
- 在捐赠者与接受者匹配,生存预测,机器人手术,器官输液评估和结果预测中分析AI/ML应用.
- 检查局限性,包括过度配套,选择偏差和验证问题.
主要成果:
- 人工智能有助于移植前的捐赠者-接受者匹配和生存预测,可能减少器官丢弃.
- 人工智能通过增强现实和3D模型来加强机器人手术的规划.
- ML评估在正常热机器输液过程中器官的质量.
- 与传统方法相比,人工神经网络在预测移植存活和排斥方面表现出卓越的准确性.
结论:
- 人工智能和机器学习已经证明了改善移植各种方面的巨大潜力.
- 目前,临床实施受到数据相关挑战的限制,例如过度匹配和偏差.
- 需要进一步的验证和标准化,以便在KT中更广泛地采用AI/ML.
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