机器学习对瘤进展的预测:我们有多可靠?
Molham Sakkal1, Abdallah Abou Hajal1
1College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates.
Computers in biology and medicine
|April 17, 2025
概括
人工智能 (AI) 正在通过应用机器学习 (ML) 来改变癌症护理,用于瘤进展建模,诊断和预后. 本综述分析了ML在瘤学中的临床整合的应用和挑战.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 癌症在瘤进展方面存在复杂的挑战.
- 人工智能 (AI) 在癌症治疗中提供了变革性的潜力.
研究的目的:
- 审查机器学习 (ML) 在瘤研究中的应用.
- 分析阻碍ML模型临床整合的挑战.
主要方法:
- 从2014年到2024年进行全面的文献搜索 (PubMed,Scopus,IEEE Xplore).
- 对87篇关于ML的精选论文进行系统查,以查看瘤进展,诊断和预后.
- 分析ML技术,包括监督和深度学习.
主要成果:
- ML适用于瘤微环境,遗传学,组织病理学和放射学数据.
- 监督学习 (SVM,随机森林) 和深度学习是关键的ML方法.
- 可靠性和准确性的挑战限制了ML模型的临床集成.
结论:
- 建议制定战略,以提高ML模型的稳定性和在瘤学中的适用性.
- 弥合技术差距对于个性化癌症治疗至关重要.
- 本综述是将ML纳入临床瘤学的资源.
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