Interface Matters: Exploring Human Trust in Health Information from Large Language Models via Text, Speech, and Embodiment

The deployment of Conversational User Interfaces (CUIs) with advanced Large Language Models (LLMs) has significantly transformed health information seeking and dissemination, facilitating immediate and interactive communication between users and digital health resources. However, while trust is crucial for adopting health advice, how the dissemination interface influences people's perceived trust in health information provided by LLMs remains unclear. To address this, we conducted a mixed-methods, within-subjects lab study (N=20) to investigate how different CUIs (i.e., a text-based, speech-based, and embodied interface) affect user-perceived trust levels when delivering health information from an identical LLM source. Our key findings showed that: (a) participants' trust levels in health information delivered were significantly variant across different interfaces; (b) there are significant correlations between trust in health-related information and trust in the delivered interface as well as the usability level of the interface; (c) the type of health questions did not affect participants' perceived trust. Besides, we identified key factors influencing trust in health information delivered through various CUIs and explored differences in how people trust health information from LLM and its dissemination. We highlight the potential of LLM-powered CUIs in supporting health-related information-seeking behaviors. This work contributes insights for ensuring effective and trustworthy personal health information-seeking in the era of LLM-powered CUIs and multi-modal information dissemination.