Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

Computer Science > Artificial Intelligence [Submitted on 10 Jun 2026] Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning Haoyang Zeng, Yuanxi Fu, Rongzhen Li, Yuming Yang, Xiao Sun, Jingwang Huang, Gujie Shao, Guohui Xiang, Quan Lu, Dongfan Ye, Xuetao Chen, Jiang Zhong, Kaiwen Wei, Zhi Xu Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.11675 [cs.AI]   (or arXiv:2606.11675v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.11675 Focus to learn more Submission history From: Haoyang Zeng [view email] [v1] Wed, 10 Jun 2026 05:39:08 UTC (8,285 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)