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LLMs fused with ontology rankers boost rare-disease diagnosis accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel fusion model that combines the diagnostic capabilities of Large Language Models (LLMs) with traditional ontology rankers for rare-disease diagnosis. This approach aims to leverage the evidence-based reasoning of ontology rankers while incorporating the differential diagnosis generation of LLMs. The fusion model analyzes ranked lists from both systems and their agreement, improving diagnostic accuracy. Experiments showed significant gains in Phenomizer Recall@1 when using the fusion model, even when paired with an LLM like DeepSeek-V4-Flash without retraining. AI

IMPACT Enhances diagnostic accuracy for rare diseases by integrating LLM capabilities with structured evidence.

RANK_REASON Research paper detailing a novel method for fusing LLMs with ontology rankers for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs fused with ontology rankers boost rare-disease diagnosis accuracy

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Research paper detailing a novel method for fusing LLMs with ontology rankers for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim, Zicheng Li, Xuanqi Peng, Fei Teng, Jiacong Mi, Honghan Wu ·

    Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

    arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLMs) can generate differential diagnoses from the same patient description, but th…

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