实现机器学习在精密瘤学中的承诺:专家对机遇和挑战的观点
Vasileios Nittas1,2, Kelly E Ormond2, Effy Vayena2
1Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, 8001, Switzerland.
BMC cancer
|February 18, 2025
概括
精确瘤学的机器学习 (ML) 是新生的,但对个性化癌症护理有希望. 克服数据访问,临床实用性和道德挑战对于其未来的整合至关重要.
科学领域:
- 精确瘤学是一门精确的专业.
- 医疗人工智能的人工智能
- 生物信息学是一种生物信息学.
背景情况:
- 机器学习 (ML) 在分析精确瘤学的复杂患者数据方面显示出潜力.
- 在数字病理学和放射学中,ML应用正在进步,在基因组和表观遗传数据分析中新兴使用.
研究的目的:
- 捕捉专家对ML在精密瘤学当前状态,挑战和未来的观点.
- 确定将ML纳入癌症治疗的机会和伦理考虑.
主要方法:
- 与学术和临床专家进行半结构面试.
- 定性数据分析以确定与精密瘤学中的ML相关的主题.
主要成果:
- 精密瘤学的ML处于早期阶段,临床整合有限.
- 专家强调了ML在改善工作流程和治疗决策方面的潜力,但指出了数据访问,临床实用性和指导方针方面的障碍.
- 关键的伦理问题包括隐私,公平,可解释性和信任.
结论:
- 机器学习为推进癌症研究和以患者为中心的护理提供了巨大的潜力.
- 解决数据访问,临床验证,监管框架和伦理问题对于实现ML在精密瘤学的未来至关重要.
关键词:
算法偏差是一种算法偏差.人工智能的人工智能是人工智能.这是一个偏见的偏见.黑盒子是一个黑盒子.伦理学 伦理学 伦理学可以解释的可解释性.机器学习是机器学习.透明度 (Opacity) 是指一个透明度.精确瘤学是一门精确的专业.隐私 隐私 隐私 隐私 隐私 隐私更多相关视频
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