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

• URL: http://arxiv.org/abs/2603.01252v1
• Date: Sun, 01 Mar 2026 20:13:09 GMT
• Title: Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation
• Authors: Liwen Sun, Xiang Yu, Ming Tan, Zhuohao Chen, Anqi Cheng, Ashutosh Joshi, Chenyan Xiong,
• Abstract summary: Large language models (LLMs) could ease the pre-diagnostic workload, but their limited domain knowledge hinders effective medical question generation.<n>We introduce a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions, KG-Followup.<n> Experiments demonstrate that KG-Followup outperforms state-of-the-art methods by 5% - 8% on relevant benchmarks in recall.
• Score: 27.44199508819891
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs [12.287636586297756]
  The recent proliferation of large language models (LLMs) holds the potential to revolutionize healthcare.<n>This paper investigates the reliability and viability of using medical knowledge graphs (KGs) for the automated factuality evaluation of LLM-generated responses.<n>We introduce FAITH, a framework designed to probe the strengths and limitations of this KG-based approach.
  arXiv  Detail & Related papers  (2025-11-16T22:58:22Z)
• MedKGEval: A Knowledge Graph-Based Multi-Turn Evaluation Framework for Open-Ended Patient Interactions with Clinical LLMs [19.12790150016383]
  We present MedKGEval, a novel multi-turn evaluation framework for clinical large language models.<n>A knowledge graph-driven patient simulation mechanism retrieves relevant medical facts from a curated knowledge graph.<n>A turn-level evaluation framework assesses each model response for clinical appropriateness, factual correctness, and safety.
  arXiv  Detail & Related papers  (2025-10-14T07:22:26Z)
• MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph [57.54231831309079]
  We introduce MedKGent, a framework for constructing temporally evolving medical Knowledge Graphs.<n>We simulate the emergence of biomedical knowledge via a fine-grained daily time series.<n>The resulting KG contains 156,275 entities and 2,971,384 relational triples.
  arXiv  Detail & Related papers  (2025-08-17T15:14:03Z)
• Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation [29.547871298286687]
  We introduce a knowledge-guided data augmentation framework that enhances the difficulty of clinical datasets by generating distractors.<n>Our approach involves multi-step, semantically informed walks on a medical knowledge graph to identify distractor paths-associations that are medically relevant but factually incorrect.<n>We apply the designed knowledge graph guided distractor generation (KGGDG) pipline, to six widely used medical QA benchmarks and show that it consistently reduces the accuracy of state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-05-31T15:51:09Z)
• Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspective [1.4913052010438639]
  We have explored the use of Large Language Models (LLMs) for imputing missing treatment relationships.<n>LLMs offer promising capabilities in knowledge augmentation, but their application in medical knowledge imputation presents significant risks.<n>Our findings highlight critical limitations, including inconsistencies with established clinical guidelines and potential risks to patient safety.
  arXiv  Detail & Related papers  (2025-03-29T02:52:17Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their abilities to directly recall and apply factual medical knowledge remains under-explored.<n>We introduce the Medical Knowledge Judgment dataset (MKJ), a dataset derived from the Unified Medical Language System (UMLS), a comprehensive repository of standardized vocabularies and knowledge graphs.<n>Through a binary classification framework, MKJ evaluates LLMs' grasp of fundamental medical facts by having them assess the validity of concise, one-hop statements.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
  We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians. Experimental results demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-08-22T17:44:40Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain [19.46334739319516]
  We study how the dual logic ability of LLMs is affected during the privatization process in the medical domain. Our results indicate that incorporating general domain dual logic data into LLMs not only enhances LLMs' dual logic ability but also improves their accuracy.
  arXiv  Detail & Related papers  (2023-09-08T08:20:46Z)
• Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study [6.10474409373543]
  We propose an innovative approach for augmenting the proficiency of Large Language Models (LLMs) in automated diagnosis generation.<n>We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge.<n>Our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems.
  arXiv  Detail & Related papers  (2023-08-28T06:05:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.