多模式整合策略在瘤学中的临床应用
Baoyi Zhang1, Zhuoya Wan2, Yige Luo1
1AbbVie Bay Area, South San Francisco, CA, United States.
Frontiers in pharmacology
|September 5, 2025
概括
人工智能 (AI) 整合了多种癌症患者的数据,包括多组和成像,以获得全面的见解. 这种多模式的方法提高了瘤学的诊断,预后和治疗策略.
科学领域:
- 癌症学
- 生物信息学
- 医学成像
- 人工智能
背景情况:
- 临床实践产生各种患者数据 (临床,基因组学,成像).
- 目前的分析通常集中在单一的数据类型上,
- 整合多式联络数据对于促进癌症治疗至关重要.
研究的目的:
- 提供有关瘤学的先进多式联通数据整合策略的概述.
- 探索人工智能驱动的多模式分析的临床潜力.
- 讨论该领域的挑战和未来方向.
主要方法:
- 单个数据模式的处理:多组,成像,临床笔记.
- 应用各种人工智能方法:机器学习,表示学习,视觉语言模型.
- 制定多式联运一体化战略,包括优缺点.
主要成果:
- 人工智能可以有效地整合复杂,异构的癌症数据.
- 多模式分析提供了比单模式方法更深入的见解.
- 为特定的数据类型和整合量身定制的AI方法.
结论:
- 多模式人工智能整合具有早期癌症检测和诊断的巨大潜力.
- 促进生物标志物的发现和临床结果的预测.
- 解决目前的挑战,并概述未来的瘤研究途径.
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