An Agentic AI Workflow for Detecting Cognitive Concerns in Real-world Data

Understand

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.