Taxonomy of Comprehensive Safety for Clinical Agents
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908570674528256 |
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| author | Seo, Jean Lee, Hyunkyung Kim, Gibaeg Han, Wooseok Yoo, Jaehyo Lim, Seungseop Shin, Kihun Yang, Eunho |
| author_facet | Seo, Jean Lee, Hyunkyung Kim, Gibaeg Han, Wooseok Yoo, Jaehyo Lim, Seungseop Shin, Kihun Yang, Eunho |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22041 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Taxonomy of Comprehensive Safety for Clinical Agents Seo, Jean Lee, Hyunkyung Kim, Gibaeg Han, Wooseok Yoo, Jaehyo Lim, Seungseop Shin, Kihun Yang, Eunho Computation and Language 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. |
| title | Taxonomy of Comprehensive Safety for Clinical Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.22041 |