LLMs for health: Perceived benefits, risks, intention to use AI chatbots, and willingness to self-disclose across sensitive health topics
For health communication researchers and chatbot designers, this study provides insights into user perceptions and behaviors regarding AI chatbots for sensitive health topics, though findings are incremental.
This study examines how topic type and individual characteristics affect perceived benefits, risks, intention to use AI chatbots, and willingness to self-disclose health information. Results from a Dutch sample (N=1,388) show that perceived benefits positively relate to intention and self-disclosure, while perceived risks negatively relate, with topic sensitivity influencing usage intentions.
AI chatbots are increasingly used for answering health-related questions. This study examines the role of topic type discussed with an AI chatbot and individual characteristics on perceived benefits and risks, intention to use an AI chatbot, and willingness to self-disclose health information. We conducted an online experiment with a 2 (topic type: physical versus psychological, between-subjects) x 2 (topic sensitivity: low versus high, within-subjects) mixed design among a Dutch representative sample (N = 1,388). Results showed that perceived benefits were positively associated with intention and willingness to self-disclose, while perceived risks were negatively associated. Moreover, participants reported higher usage intentions for low-sensitive topics compared to high-sensitive topics. Furthermore, perceptions, intention, and willingness to self-disclose varied by individual characteristics. Overall, our findings suggest that intentions to use AI chatbots and self-disclosure of health-related information are primarily related to perceived benefits and risks and to personal characteristics rather than to topic type.