超越相关性：迈向生物医学中的因果大语言模型智能体
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
AI总结：
本文提出构建生物医学中的因果大语言模型智能体，通过整合多模态数据与干预推理克服相关性局限，以加速药物发现和实现个性化医疗。
摘要：
大语言模型（LLMs）在生物医学领域展现出潜力，但缺乏真正的因果理解，而是依赖于相关性。本文设想了一种因果LLM智能体，它整合多模态数据（文本、图像、基因组学等）并执行基于干预的推理以推断因果关系。实现这一目标需要克服关键挑战：设计安全、可控的智能体框架；开发用于因果评估的严格基准；整合异构数据源；以及将LLMs与结构化知识（知识图谱）和形式化因果推断工具协同结合。此类智能体可能开启变革性机遇，包括通过自动化假设生成和模拟加速药物发现，以及通过患者特定的因果模型实现个性化医疗。这一研究议程旨在促进跨学科努力，将因果概念与基础模型相连接，以开发可靠的AI合作伙伴，推动生物医学进步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
