人工智能和机器学习在骨转移管理中:叙述性审查
Halil Bulut1, Serdar Demiröz2, Enes Kanay3
1Cerrahpasa School of Medicine, Istanbul University Cerrahpasa, 34098 Istanbul, Turkey.
Current oncology (Toronto, Ont.)
|January 27, 2026
概括
人工智能 (AI) 和机器学习 (ML) 在管理骨转移方面表现有前途,从检测到风险评估. 对验证的多式联络人工智能工具进行进一步的研究是必要的,以改善患者护理.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地用于诊断和管理骨转移.
- 应用范围包括病变检测,细分,预后建模,骨折风险评估和手术决策支持.
- 快速发展和异质的文献对临床上下文化提出了挑战.
研究的目的:
- 在骨转移管理中对AI/ML应用进行叙事审查.
- 专注于临床相关的问题,如病变检测,细分和骨相关事件预测.
- 优先考虑最近的研究报告模型性能和临床翻译潜力.
主要方法:
- 叙事文学评论. 叙事文学评论. 叙事文学评论. 叙事文学评论.
- 专注于AI/ML在骨转移检测,细分,预后和手术决策支持方面的应用.
- 优先考虑最近的,同行评审的研究报告模型性能和临床翻译.
主要成果:
- 大多数研究集中在基于成像的诊断和使用放射学和深度学习的病变细分,具有高的内部但有限的外部验证.
- 关于预后模型和骨折风险估计的新兴研究处于概念验证的早期阶段.
- 很少有人工智能框架被整合到常规工作流程中;可解释性,偏见和成本效益很少被评估.
结论:
- 人工智能和机器学习工具为标准化成像评估和个性化骨转移管理提供了巨大的潜力.
- 未来的研究应该强调外部验证的多式模式和AI增强的临床决策工具.
- 开发人工智能增强的替代Mirels评分,并纳入可解释性和成本效益分析的发展至关重要.
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