MA-RAG: Multi-Agent Retrieval-Augmented Generation for Query-Driven Summarization of Longitudinal Parkinson's Disease Assessments
MA-RAG is a query-driven multi-agent retrieval-augmented generation framework designed for summarizing longitudinal Parkinson's disease clinical assessments The framework decomposes clinical reasoning into domain-specialized agents, combining structured fact extraction with a final verification stage MA-RAG supports four clinical analysis tasks: single-session, trajectory, comparison, and cohort summarization MA-RAG achieved a 122% relative increase in Fact Precision (0.436 to 0.990) and reduced
Analysis
TL;DR
- MA-RAG is a query-driven multi-agent retrieval-augmented generation framework designed for summarizing longitudinal Parkinson's disease clinical assessments
- The framework decomposes clinical reasoning into domain-specialized agents, combining structured fact extraction with a final verification stage
- MA-RAG supports four clinical analysis tasks: single-session, trajectory, comparison, and cohort summarization
- MA-RAG achieved a 122% relative increase in Fact Precision (0.436 to 0.990) and reduced Hallucination Rate by 98% (0.564 to 0.010) compared to baselines
- Clinical experts consistently rated MA-RAG's outputs highest for organization and clinical usefulness
Why It Matters
This work addresses a critical gap in applying LLMs to clinical domains, where factual accuracy and temporal consistency are paramount. The multi-agent RAG approach demonstrates that decomposing complex reasoning tasks into specialized agents can dramatically reduce hallucinations while improving factual precision, offering a scalable template for other medical and structured-data summarization applications.
Technical Details
- MA-RAG decomposes clinical reasoning into domain-specialized agents that handle different aspects of Parkinson's disease assessment interpretation, rather than relying on a single monolithic LLM
- The framework integrates structured fact extraction from longitudinal clinical data with retrieval-augmented generation, followed by a final verification stage to ensure clinical grounding and temporal consistency
- Evaluation uses four objective metrics: Fact Precision, Hallucination Rate, Temporal Fidelity, and Semantic Similarity, supplemented by subjective evaluations from clinical experts
- Baselines include Traditional methods, RAG-only, and Single-agent RAG, with MA-RAG outperforming all across every measured dimension
- The system supports four distinct clinical analysis tasks: single-session summarization, trajectory analysis, comparison across visits, and cohort-level summarization
Industry Insight
- Multi-agent RAG architectures represent a promising direction for deploying LLMs in high-stakes domains like healthcare, where hallucination reduction is non-negotiable; practitioners should consider agent decomposition as a strategy for complex reasoning tasks
- The 122% improvement in Fact Precision and 98% hallucination reduction demonstrate that specialized multi-agent frameworks can close the reliability gap that currently limits clinical LLM adoption
- This work validates query-driven summarization for longitudinal structured data, suggesting broader applicability to other chronic disease monitoring and electronic health record analysis use cases
Disclaimer: The above content is generated by AI and is for reference only.