User Preferences in Conversational AI for Healthcare: Insights from an Interview Study

DOI

Chatbot-based symptom diagnosis apps are becoming increasingly popular, yet concerns remain around usability and user trust. This study explores user preferences regarding chatbot characteristics using a rhetorical structure in symptom diagnosis chatbots. We conducted 16 semi-structured interviews across two use-case groups (varying in symptom severity) and analyzed 69 user reviews from four chatbot applications. Findings show that users consistently valued logos (clear explanations, structured dialogue) and ethos (consistency, next steps), while pathos (emotional support) became more important in high-severity scenarios. Similarly, logos-based characteristics were pivotal in all phases, but ethos became prominent in the third phase – diagnosis delivery. Interviews uncovered various themes around dialogue management, interaction design, and personalization needs. App reviews supported these findings, highlighting gaps in transparency, empathy, and usability. Based on these insights, we propose design guidelines and visualize interaction concepts that align with rhetorical strategies to improve trust and effectiveness in health-focused conversational agents.