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arXiv · 2509.22041

Taxonomy of Comprehensive Safety for Clinical Agents

Jean Seo·Hyunkyung Lee·Gibaeg Kim·Wooseok Han·Jaehyo Yoo·Seungseop Lim·Kihun Shin·Eunho Yang
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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 is a taxonomy that can cover 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 useful insights about train data distribution and pretrained knowledge of base models.

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Jean Seo, Hyunkyung Lee, Gibaeg Kim, Wooseok Han, Jaehyo Yoo, Seungseop Lim, Kihun Shin, Eunho Yang. 2025-09-26. Taxonomy of Comprehensive Safety for Clinical Agents. https://arxiv.org/abs/2509.22041

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