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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.13690 |
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| _version_ | 1866915951367159808 |
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| author | Guo, Yue Wang, Fanfu Lv, Jianwei Shi, Xincheng Li, Yuchen Wang, Youya Zeng, Yunsheng Liu, Yujing Qiao, Yunhao Li, Gen Wang, Junfeng Yuan, Bo |
| author_facet | Guo, Yue Wang, Fanfu Lv, Jianwei Shi, Xincheng Li, Yuchen Wang, Youya Zeng, Yunsheng Liu, Yujing Qiao, Yunhao Li, Gen Wang, Junfeng Yuan, Bo |
| contents | Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high maintenance costs and low generalization capability. Recently, Large Language Models (LLMs) have been widely adopted in healthcare due to their extensive knowledge reserves, retrieval, and communication capabilities. While LLMs show promise and excel at medical benchmarks, their diagnostic reasoning and inquiry skills are constrained. To mitigate this issue, we propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills. Its training involves a two-stage process: SFT, followed by RL with a tailored reward function. We also introduce a benchmark to evaluate both diagnostic reasoning and inquiry. Our experiments demonstrate that the Dr. Assistant outperforms open-source models and achieves competitive performance to closed-source models, providing an effective solution for clinical diagnostic inquiry guidance. Project information can be found at: https://github.com/YGswu/Dr.-Assistant . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning Guo, Yue Wang, Fanfu Lv, Jianwei Shi, Xincheng Li, Yuchen Wang, Youya Zeng, Yunsheng Liu, Yujing Qiao, Yunhao Li, Gen Wang, Junfeng Yuan, Bo Computation and Language Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high maintenance costs and low generalization capability. Recently, Large Language Models (LLMs) have been widely adopted in healthcare due to their extensive knowledge reserves, retrieval, and communication capabilities. While LLMs show promise and excel at medical benchmarks, their diagnostic reasoning and inquiry skills are constrained. To mitigate this issue, we propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills. Its training involves a two-stage process: SFT, followed by RL with a tailored reward function. We also introduce a benchmark to evaluate both diagnostic reasoning and inquiry. Our experiments demonstrate that the Dr. Assistant outperforms open-source models and achieves competitive performance to closed-source models, providing an effective solution for clinical diagnostic inquiry guidance. Project information can be found at: https://github.com/YGswu/Dr.-Assistant . |
| title | Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.13690 |