Graph retrieval augmented large language models for facial phenotype associated rare genetic disease.

Many rare genetic diseases have recognizable facial phenotypes that serve as diagnostic clues. While Large Language Models (LLMs) have shown potential in healthcare, their application to rare genetic diseases still faces challenges like hallucination and limited domain knowledge. To address these challenges, Retrieval-Augmented Generation (RAG) is an effective method, while Knowledge Graphs (KGs) provide more accurate and reliable information. In this paper, we constructed a Facial Phenotype Knowledge Graph (FPKG) including 6143 nodes and 19,282 relations and incorporate RAG to alleviate the hallucination of LLMs and enhance their ability to answer rare genetic disease questions. We evaluated eight LLMs across four tasks: domain-specific QA, diagnostic tests, consistency evaluation, and temperature analysis. The results showed that our approach improves both diagnostic accuracy and response consistency. Notably, RAG reduces temperature-induced variability by 53.94%. This study demonstrates that LLMs can effectively incorporate domain-specific KGs to enhance accuracy, and consistency, thereby improving diagnostic decision-making.