@inproceedings{sun-etal-2026-aida,
    title = "{AIDA}-{SEAT}: Towards Reliable {AI} Doctor Assistant via State-Evaluation-Action Tree Enhanced {LLM}s in Online Hospital",
    author = "Sun, Lianxin  and
      Ying, Xiaoying  and
      Yu, Guangya  and
      Zhang, Weiyan  and
      Guan, Chenhao  and
      He, Hao  and
      SHANG, Mingxi  and
      Li, Jianhua  and
      Wang, ChunMing  and
      Ruan, Tong",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.48/",
    pages = "704--715",
    ISBN = "979-8-89176-394-4",
    abstract = "Artificial intelligence doctor assistants (AIDAs) help streamline clinical decision-making and reduce physician workload. While existing systems primarily utilize Large Language Models (LLMs) or retrieval-augmented generation (RAG), these methods typically retrieve static facts{---}whether as text passages or structured graphs{---}lacking the explicit logical pathways essential for multi-step reasoning. In this paper, we propose the AIDA-SEAT framework to provide reliable clinical decision-making support. First, we design the state-evaluation-action tree (SEAT), which covers diagnosis, treatment, and examination. To develop this tree, we refine and transform SEAT collected from medical documents and doctors. Then, we propose an adaptive method to select optimal trees tailored to the current patients' state. Finally, we leverage LLMs to perform state assessment, evaluation, and action execution based on the tree, thereby generating reliable responses. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built dataset. Our method achieves 1.01{\%} higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods. Furthermore, analysis of 200 consultation records during deployment on an online hospital revealed that system-assisted responses are 24.16 seconds faster on average than manual ones, improving efficiency by 26.85{\%}."
}