人工智能用于预测骨科手术后并发症:临床应用,挑战和未来方向的审查
Aviral C Sharma1, Amta Azeem1, Ibrahim H Omari2
1School of Medicine, Ponce Health Sciences University, St. Louis, USA.
Cureus
|January 29, 2026
概括
人工智能 (AI) 模型在预测骨科手术术后并发症方面表现优异,与传统的统计方法相比. 负责任的采用需要一个验证,整合和监管的框架.
科学领域:
- 整形外科手术 整形外科手术
- 医疗人工智能 医疗人工智能
- 预测分析是一种预测分析.
背景情况:
- 术后并发症,如感染和静脉血栓栓塞 (VTE) 显著增加了骨科手术的发病率和医疗费用.
- 传统的风险分层工具对于个性化的患者预测缺乏准确性.
- 人工智能 (AI) 和机器学习 (ML) 为预测这些并发症提供了潜在的进步.
研究的目的:
- 进行AI/ML模型与传统统计模型的比较合成,用于预测骨科手术中术后并发症.
- 提出一个验证,整合和监管 (VIR) 框架,以便在这个领域负责任地采用人工智能.
主要方法:
- 2017年1月至2025年7月的同行评审研究的结构化叙事综述,来源于PubMed,Scopus和谷歌学者.
- 纳入标准侧重于AI/ML模型,预测骨科队伍中术前/术后并发症,报告定量性表现指标.
- 排除仅用于成像研究,非骨科队列和非原始研究报告.
主要成果:
- 人工智能驱动的模型通常在预测诸如假肢关节感染,输血需求,植入物失败和非联合等结果方面优于传统的统计方法.
- 在AI/ML模型中,曲线下的报告面积 (AUC) 值通常在0.75-0.90之间,而在传统回归模型中则为0.60-0.75.
- 采用挑战包括过度安装,有限的多中心验证,校准和实用性的不一致报告,以及可解释性和互操作性的问题.
结论:
- 人工智能/ML模型在改善骨科手术中术后并发症的预测方面显示出显著的前景.
- 包括多中心验证,无工作流集成和监管监督在内的结构化方法对于人工智能工具的负责任翻译至关重要.
- 未来的研究应该探索联合学习,混合临床医生-人工智能系统和以公平为中心的验证,以加强人工智能在骨科护理中的整合.
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