让大模型从猜相关转向懂因果，推动生物医药智能决策。
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
- 构建能干预推理的因果代理，融合文本、图像、基因等多模态数据。
- 提出需解决安全框架、评估基准、异构数据融合等关键挑战。
- 适合医药研发、精准医疗领域研究者，助力药物发现与个性化治疗。
大型语言模型（LLMs）在生物医学领域展现出巨大潜力，但缺乏真正的因果理解，仅依赖相关性。本文提出构建集成多模态数据（文本、图像、基因组等）并具备干预推理能力的因果大模型代理，以推断因果关系。这需要克服若干核心挑战：设计安全可控的代理框架；建立严格的因果评估基准；整合异构数据源；以及协同结合知识图谱（KGs）与形式化因果推断工具。此类代理有望实现变革性应用，如通过自动化假设生成与模拟加速药物研发，基于患者个体因果模型实现个性化医疗。本研究议程旨在推动跨学科协作，弥合因果概念与基础模型之间的鸿沟，打造可信赖的生物医学人工智能伙伴。
原文摘要 · Abstract (English)
Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics, etc.) and perform intervention-based reasoning to infer cause-and-effect. Addressing this requires overcoming key challenges: designing safe, controllable agentic frameworks; developing rigorous benchmarks for causal evaluation; integrating heterogeneous data sources; and synergistically combining LLMs with structured knowledge (KGs) and formal causal inference tools. Such agents could unlock transformative opportunities, including accelerating drug discovery through automated hypothesis generation and simulation, enabling personalized medicine through patient-specific causal models. This research agenda aims to foster interdisciplinary efforts, bridging causal concepts and foundation models to develop reliable AI partners for biomedical progress.
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