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        <title>Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset</title>
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    <abstract>Clinical dialogue systems are increasingly vital for patient education and follow-up care; however, effective responses often depend on the clinical context that patients often fail to provide in detail. Responding directly to vague messages can therefore lead to generic or clinically misaligned advice, a challenge that is particularly pronounced in post-op oral cancer (OC) care due to speech impairment and functional limitations. Moreover, post-op OC patients often experience psychological distress, making safety-aware language more likely to arise in dialogue. Dialogue systems in this setting must therefore address both clarifying missing clinical context and ensuring psychological safety. We propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation. Expert evaluations show that the proposed system improves the quality and clinical appropriateness of generated responses relative to strong baselines, while the safety module closely aligns with expert judgments on clinically concerning utterances. Furthermore, we release a clinically curated Chinese post-op OC QA dataset with expert-validated annotations, which we use throughout our experiments.</abstract>
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%0 Conference Proceedings
%T Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset
%A Liu, Tzu-Chi
%A Yang, Hui-Ying
%A Shun, Shiow-Ching
%A Chen, Yu-Chi
%A Chen, Lu-Yen Anny
%A Chen, Yong-Sheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-safety
%X Clinical dialogue systems are increasingly vital for patient education and follow-up care; however, effective responses often depend on the clinical context that patients often fail to provide in detail. Responding directly to vague messages can therefore lead to generic or clinically misaligned advice, a challenge that is particularly pronounced in post-op oral cancer (OC) care due to speech impairment and functional limitations. Moreover, post-op OC patients often experience psychological distress, making safety-aware language more likely to arise in dialogue. Dialogue systems in this setting must therefore address both clarifying missing clinical context and ensuring psychological safety. We propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation. Expert evaluations show that the proposed system improves the quality and clinical appropriateness of generated responses relative to strong baselines, while the safety module closely aligns with expert judgments on clinically concerning utterances. Furthermore, we release a clinically curated Chinese post-op OC QA dataset with expert-validated annotations, which we use throughout our experiments.
%U https://aclanthology.org/2026.findings-acl.902/
%P 18130-18142

Markdown (Informal)

[Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset](https://aclanthology.org/2026.findings-acl.902/) (Liu et al., Findings 2026)

• Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset (Liu et al., Findings 2026)

ACL

• Tzu-Chi Liu, Hui-Ying Yang, Shiow-Ching Shun, Yu-Chi Chen, Lu-Yen Anny Chen, and Yong-Sheng Chen. 2026. Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18130–18142, San Diego, California, United States. Association for Computational Linguistics.