Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis
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 their predictions lack an equally clear evidence trail. Rather than asking which system should replace the other, we ask whether an LLM can improve the ranker without giving up its evidence. Our behavior-based fusion model examines the two ranked lists, their agreement, and the ontology support behind each candidate, and learns how much to rely on each system for the individual case. Before comparison, we remove a documented test-set leakage pathway caused by ben...
| Authors | Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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