桥梁人工智能和生物学:基础模型满足人类生理学和疾病
Orr Ashenberg1, Ramnik J Xavier2
1Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Med (New York, N.Y.)
|January 10, 2026
概括
基础模型在临床环境中扎,因为它们缺乏因果推理. 结合生物机制的混合AI模型可以改善疾病预测和治疗策略.
科学领域:
- 医学中的人工智能
- 计算生物学是一种计算生物学.
- 翻译科学是翻译的科学.
背景情况:
- 目前的AI基础模型,在观察数据上训练,在基于关联的预测中表现出色,但未能捕捉临床应用至关重要的因果关系.
- 人工智能模型从基准模型慢慢转化为现实世界的临床影响,突出了理解疾病进展和治疗疗效的根本局限性.
研究的目的:
- 解决临床环境中基于相关性的AI模型的局限性.
- 提出混合人工智能模型,这些模型包含因果推理的生物机制.
- 为了使有效的疾病干预和个性化医疗的机制理解.
主要方法:
- 开发混合人工智能模型,将深度学习与机械生物学表示集成在一起.
- 在AI框架内使用因果推理技术.
- 在不同的数据集上培训和验证模型,包括观察和机械信息.
主要成果:
- 证明混合模型可以识别纯相关性方法错过的因果路径.
- 展示了混合模型为疾病动态提供机械洞察力的潜力.
- 与传统基础模型相比,突出提高了疾病过程和治疗反应的预测准确性.
结论:
- 混合人工智能模型通过使因果推理成为可能,为推进临床翻译提供了一个有希望的途径.
- 将生物机制整合到人工智能中对于实现机械学理解和推动有效的疾病干预至关重要.
- 未来的研究应该专注于开发和验证这些混合方法,以获得广泛的临床影响.
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