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人工智能免疫学家在这里:他们准备好了吗?

Jacob Kim¹, Ao Huang^1,2, John S Tsang^1,2,3,4

¹Center for Systems and Engineering Immunology (CSEI) and Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.

Science immunology

|December 5, 2025

概括

大型语言模型 (LLM) 可以自动化研究任务,但与新的生物学见解作斗争. 多代理系统和人类-人工智能合作的进步显示出未来研究发现的前景.

科学领域:

人工智能在生物研究中的应用
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

大型语言模型 (LLM) 为自动化复杂的研究任务提供了巨大的潜力.
目前的LLM能力包括文献审查,数据挖掘,代码生成和知识总结.
然而,在产生原始的生物学假设和见解方面,LLMs面临着局限性.

研究的目的:

评估生物研究中基于LLM的AI代理的当前能力和局限性.
探索新兴的解决方案,以加强人工智能在科学发现中的作用.
突出多代理系统和人与代理合作的潜力.

主要方法:

在科学研究当前的LLM应用的审查.
分析假设生成中的局限性.
探索多代理系统的进步.
检查人类-代理合作框架.

主要成果:

基于LLM的AI代理人擅长于自动化常规研究任务.
在LLM产生新生物假设的能力方面存在重大差距.
多代理系统和人-代理协作正在成为改进的关键领域.

结论:

基于LLM的AI代理是研究自动化的有价值工具,但需要进一步开发以生成假设.
未来的研究应该专注于整合多代理系统和人类-人工智能协作,以克服当前的局限性.
增强的人工智能框架有望促进生物发现和洞察力产生.

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