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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.14638 |
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| _version_ | 1866915625821011968 |
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| author | Yang, Tao Huang, Dandan Lin, Yunting Wu, Pengfei Wu, Zhikun Ma, Gangyuan Lu, Yulan Dong, Xinran Li, Dingpeng Ge, Junshuang Zhang, Zhiyan Huang, Xuanzhao Nong, Wenyan Zhou, Yao Tang, Hui Yang, Hongxi Zhang, Shijie Li, Juan Cao, Xiaojun Yang, Lin Gao, Xia Xu, Kaishou Gu, Xiaoqiong Zhang, Wen Xia, Huimin Liu, Li Zhou, Wenhao Li, Mulin Jun |
| author_facet | Yang, Tao Huang, Dandan Lin, Yunting Wu, Pengfei Wu, Zhikun Ma, Gangyuan Lu, Yulan Dong, Xinran Li, Dingpeng Ge, Junshuang Zhang, Zhiyan Huang, Xuanzhao Nong, Wenyan Zhou, Yao Tang, Hui Yang, Hongxi Zhang, Shijie Li, Juan Cao, Xiaojun Yang, Lin Gao, Xia Xu, Kaishou Gu, Xiaoqiong Zhang, Wen Xia, Huimin Liu, Li Zhou, Wenhao Li, Mulin Jun |
| contents | Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1%, such as imaging, interventions, functional tests) underpinning many correct diagnoses. This work advances a narrative first, knowledge integrated reasoning paradigm that shortens the diagnostic odyssey and enables auditable, clinically translatable decision support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases Yang, Tao Huang, Dandan Lin, Yunting Wu, Pengfei Wu, Zhikun Ma, Gangyuan Lu, Yulan Dong, Xinran Li, Dingpeng Ge, Junshuang Zhang, Zhiyan Huang, Xuanzhao Nong, Wenyan Zhou, Yao Tang, Hui Yang, Hongxi Zhang, Shijie Li, Juan Cao, Xiaojun Yang, Lin Gao, Xia Xu, Kaishou Gu, Xiaoqiong Zhang, Wen Xia, Huimin Liu, Li Zhou, Wenhao Li, Mulin Jun Computation and Language Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1%, such as imaging, interventions, functional tests) underpinning many correct diagnoses. This work advances a narrative first, knowledge integrated reasoning paradigm that shortens the diagnostic odyssey and enables auditable, clinically translatable decision support. |
| title | A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.14638 |