Work

Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation

Year: 2026

Type: conference-paper

Abstract: Clinical diagnosis is time-consuming, requiring intensive interactions between patients and medical professionals.While large language models (LLMs) could ease the pre-diagnostic workload, their limited domain knowledge hinders effective medical question generation.We introduce a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions, KG-Followup, serving as a critical module for the pre-diagnostic assessment.The structured medical domain knowledge graph serves as a seamless patch-up to provide professional domain expertise upon which the LLM can reason.Experiments demonstrate that KG-Followup outperforms state-of-the-art methods by 5% -8% on relevant benchmarks in recall.

Source: Findings of the Association for Computational Linguistics: EACL

DOI: 10.18653/v1/2026.findings-eacl.43