@inproceedings{guo-etal-2026-dr,
    title = "Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning",
    author = "Guo, Yue  and
      Wang, Fanfu  and
      Lv, Jianwei  and
      Shi, Xincheng  and
      Li, Yuchen  and
      Wang, Youya  and
      Zeng, Yunsheng  and
      Liu, Yujing  and
      Qiao, Yunhao  and
      Li, Gen  and
      Wang, Junfeng  and
      Yuan, Bo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1826/",
    pages = "36624--36658",
    ISBN = "979-8-89176-395-1",
    abstract = "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."
}