AI 中文总结
本文提出人在环智能体框架，结合领域知识与编码素养，利用智能体式大语言模型生成R代码，通过美国超级基金场地案例验证其可提升环境健康研究结果的严谨性与可复现性。
AI 中文摘要
能够规划并执行多步骤分析任务的智能体式人工智能（AI）系统，正越来越多地被环境健康研究者使用，但其在实际应用中的可靠性尚未得到充分探索。本文介绍了一种用于环境健康研究的人在环智能体框架，该框架涉及对AI生成的数据分析代码和结果进行每一步的审查、验证与修正——这一过程与传统研究团队的指导结构相呼应。该方法为在日常数据密集型环境健康研究中严格且可复现地使用智能体式AI提供了途径。我们通过一项分析美国超级基金场地含氮有机污染物的案例研究来说明该框架，该化学家族与新兴的轮胎衍生污染物6PPD和6PPD-醌相关。我们使用智能体式大语言模型生成用于数据过滤、空间制图和聚类分析的R代码，记录了初始智能体式AI输出得益于人在环过程以产生更严格、可复现结果的实例。我们得出结论，有效使用智能体式AI既需要领域专业知识来构建问题并评估输出，也需要编码素养来指导AI的方法，同时概述了智能体式AI工作流推进环境健康科学的未来机遇。
英文摘要
Agentic artificial intelligence (AI) systems that are capable of planning and executing multi-step analytical tasks are increasingly available to environmental health researchers, but their reliability in real-world practice has not been fully explored. This paper describes a human-in-the-loop agentic framework for environmental health research, involving the review, verification, and correction of AI-generated data analysis code and results at each step - a process that mirrors the mentorship structure of traditional research teams. This approach offers a path toward rigorous, reproducible use of agentic AI in routine data-rich environmental health research. We illustrate this framework through a case study analyzing nitrogenous organic contaminants at U.S. Superfund sites, a chemical family linked to the emerging tire-derived contaminants 6PPD and 6PPD-quinone. Using an agentic large language model to generate R code for data filtering, spatial mapping, and cluster analysis, we document instances where initial agentic AI outputs benefited from a human-in-the-loop process to produce more rigorous and reproducible results. We conclude that effective use of agentic AI requires both domain expertise to frame questions and evaluate outputs, and coding literacy to guide the AI's approach, while outlining future opportunities for agentic AI workflow to advance the environmental health sciences.
Comments41 pages, 3 figures
