Can multi-agent LLMs improve cognitive concern detection?
An Agentic AI Workflow for Detecting Cognitive Concerns in Real-world Data
This research explores using a multi-agent AI system and LLMs to detect cognitive concerns from patient clinical notes. The system uses specialized agents for prompt refinement, evaluation, specificity improvement, sensitivity improvement, and summarization. Compared to an expert-driven approach, the multi-agent system achieved similar accuracy with fewer prompt iterations. Key points for LLM-based multi-agent systems include: specialized agents can improve prompt engineering and model performance; iterative refinement is crucial; and this approach can automate tasks, improving efficiency in areas like clinical screening. However, challenges like overfitting and handling non-conforming LLM outputs need further investigation.