RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation
Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based phenotype extraction or retrieval-augmented generation, which suffer from critical information loss due to predefined ontologies, retrieval bottlenecks, and a lack of diagnostic logic. To address these challenges, we introduce RareDxR1, an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. We design a progre...
| Authors | Deyang Jiang, Haoran Wu, Ziyi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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