A self-correcting Agentic Graph RAG for clinical decision support in hepatology

Clinical decision-making in hepatology is currently challenged by the rapid expansion of medical knowledge and the limitations of Large Language Models (LLMs), specifically their unreliability and tendency to hallucinate. Furthermore, standard Retrieval-Augmented Generation (RAG) paradigms often fail to effectively leverage complex medical knowledge structures.

To address these issues, we propose an Agentic Graph RAG framework built upon a clinically-verified hepatology knowledge graph. Our approach utilizes a state-driven agentic system employing a self-correcting "retrieve-evaluate-refine" loop. Within this workflow, agents dynamically generate, semantically validate, assess, and iteratively optimize graph search strategies to construct a comprehensive and accurate context, which is then used by an LLM to generate reliable responses.

The framework was evaluated on a custom dataset of clinical questions. It significantly outperformed baseline models (including GPT-4, standard RAG, and Graph RAG) across all evaluation metrics. Specifically, our model achieved superior scores in faithfulness (0.94), context recall (0.92), and answer relevancy (0.91).

This agentic approach effectively mitigates LLM hallucinations and provides accurate, interpretable answers. These findings demonstrate the framework's potential as a robust, next-generation intelligent clinical decision support tool for hepatology.