"Retrieval-Augmented Generation (RAG) has become the default pattern for grounding LLMs in external knowledge, but healthcare exposes the limits of this approach due to its complex data requirements."
"The GraphRAG architecture combines a materialized knowledge graph, graph neural networks, and a LangGraph-based agent to deliver traceable, explainable answers while keeping sensitive data under governance control."
"A Neo4j knowledge graph serves as the structural backbone that integrates heterogeneous healthcare data sources, allowing for multi-hop reasoning across clinical ontologies and patient histories."
GraphRAG architecture addresses the limitations of traditional Retrieval-Augmented Generation in healthcare by utilizing a Neo4j knowledge graph to integrate diverse data sources. This architecture supports multi-hop reasoning across clinical ontologies and patient histories while ensuring data governance. It combines graph neural networks and a LangGraph-based agent to provide traceable and explainable answers. The knowledge graph serves as a unifying layer, allowing for the integration of structured and unstructured data, which is essential for effective clinical reasoning in a regulated environment.
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