# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 1 Introduction  
To emancipate productivity and reduce reliance on human labor, stakeholders in the Artificial Intelligence (AI) industry have increasingly invested in chatbot applications, driving the rapid growth of dialogue agents in recent years. Conversational chatbots can provide automated responses and interact with users via text, image, or voice. During the past pandemic, the proliferating demand for 24/7 online services underscored the importance of digital platforms in supporting global epidemic prevention efforts [1, 2]. The debut of ChatGPT-3.5, an advanced dialogue application developed by OpenAI, has further promoted chatbot research and expanded discourse in the entire range [3]. These automated conversational services - equipped with human language understanding and analysis capabilities-- have been applied in healthcare to help patients access medical information, explore treatment options, schedule appointments, and even manage aspects of patient care. Chatbots have become a significant foundational technology in the ongoing digital transformation of healthcare. Despite chatbots' potential to transform healthcare and improve patient outcomes, their deployment also raises important safety issues [4]. Recognizing the risks and limitations associated with these technologies [5] and approaching chatbot-generated responses with caution [6] is essential. Current AI technologies still fall short of fully understanding human language and often struggle to precisely infer user intent [7; 8]. This limitation poses the risk of inappropriate or inaccurate responses, primarily due to the mismatch between the complexity of natural human language and persistent bottlenecks in natural language understanding. On the one hand, chatbots' comprehension heavily depends on the dialogue corpus designed by system developers. Given the virtually limitless range of user queries, some intents will inevitably be overlooked. On the other hand, a healthcare chatbot is usually limited to responding within a specific scope [9; 10], offering basic medical knowledge, advice, and solutions to common ailments. There exist intents that are not allowed to be answered. In such high-risk environments, it is unreasonable to anticipate and respond to every query intent. Designers would not expect a chatbot uncontrollably to address intents that are uncertain, inappropriate, or outside their authorized domain. Therefore, the ability to appropriately handle these queries is a crucial requirement in the design of healthcare chatbots. Those uncertain or impermissible intents are unknown for chatbots to recognize because the intent information and utterance samples are absent from the corpus. In this study, we aim to design an unknown intent detection model that facilitates chatbots to identify and approximately respond to such queries, thereby alleviating the risk of medical errors in healthcare settings. Existing research has developed a paradigm that learns spatial transformations of query statements to construct intent decision boundaries, aiming to filter out queries lacking known intents [11; 12]. However, these approaches primarily rely on intent-related textual features extracted from user queries [11; 13; 14; 15], which may be insufficient in healthcare scenarios where conversations often require domain-specific expertise and deeper contextual understanding. Most users are unaware of dialogue system developers' design logic and typically lack medical expertise. Hence, their queries may not accurately express their intent, making it more challenging for chatbots to respond reliably. We argue that user-generated descriptions alone often fail to provide sufficient intent information or define clear boundaries in the transformed representation space. To address the above issue, we focus on algorithmic innovation for unknown intent detection. Including knowledge from multiple perspectives to enhance unknown intent detection in user queries may be a feasible direction.