@inproceedings{seo-etal-2025-taxonomy,
    title = "Taxonomy of Comprehensive Safety for Clinical Agents",
    author = "Seo, Jean  and
      Lee, Hyunkyung  and
      Kim, Gibaeg  and
      Han, Wooseok  and
      Yoo, Jaehyo  and
      Lim, Seungseop  and
      Shin, Kihun  and
      Yang, Eunho",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.124/",
    pages = "1762--1779",
    ISBN = "979-8-89176-333-3",
    abstract = "Safety is a paramount concern in clinical chatbot applications, where inaccurate or harmful responses can lead to serious consequences. Existing methods{---}such as guardrails and tool-calling{---}often fall short in addressing the nuanced demands of the clinical domain. In this paper, we introduce TACOS(Taxonomy of Comprehensive Safety for Clinical Agents), a fine-grained, 21-class taxonomy that integrates safety filtering and tool selection into a single user intent classification step. TACOS covers a wide spectrum of clinical and non-clinical queries, explicitly modeling varying safety thresholds and external tool dependencies. To validate our taxonomy, we curate a TACOS-annotated dataset and perform extensive experiments. Our results demonstrate the value of a new taxonomy specialized for clinical agent settings, and reveal valuable insights about train data distribution and pretrained knowledge of base models."
}