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

arXiv: https://arxiv.org/abs/2502.01789 · HTML: https://mlsanity.com/paper/2502.01789/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 6 of 6)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Generated Knowledge Prompting for Commonsense Reasoning](https://mlsanity.com/paper/2110.08387/) (2021)
- [Prompt Engineering for Healthcare: Methodologies and Applications](https://mlsanity.com/paper/2304.14670/) (2023)
- [Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models](https://mlsanity.com/paper/2411.02036/) (2024)

## Built on by (most cited first, 3 of 3)

- [Towards a HIPAA Compliant Agentic AI System in Healthcare](https://mlsanity.com/paper/2504.17669/) (2025)
- [An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives](https://mlsanity.com/paper/2604.15369/) (2026)
- [First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows](https://mlsanity.com/paper/2604.18038/) (2026)

## Abstract (arXiv)

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.