Early identification of cognitive concerns is critical but often hindered by subtle symptom presentation. This study developed and validated a fully automated, multi-agent AI workflow using LLaMA 3 8B to identify cognitive concerns in 3,338 clinical notes from Mass General Brigham. The agentic workflow, leveraging task-specific agents that dynamically collaborate to extract meaningful insights from clinical notes, was compared to an expert-driven benchmark. Both workflows achieved high classification performance, with F1-scores of 0.90 and 0.91, respectively. The agentic workflow demonstrated improved specificity (1.00) and achieved prompt refinement in fewer iterations. Although both workflows showed reduced performance on validation data, the agentic workflow maintained perfect specificity. These findings highlight the potential of fully automated multi-agent AI workflows to achieve expert-level accuracy with greater efficiency, offering a scalable and cost-effective solution for detecting cognitive concerns in clinical settings.

本研究针对认知问题早期识别中的挑战，开发并验证了一种基于LLaMA 3 8B的全自动多代理人工智能工作流程。该工作流程在分析来自马萨诸塞州总医院布莱根的3,338个临床记录中，表现出优于传统专家驱动方法的分类性能，尤其在特异性和效率方面，展示了其在临床环境中识别认知问题的巨大潜力。

用于检测现实世界数据中认知问题的自主人工智能工作流程