peer reviewed · Pervasive Computing Technologies for Healthcare (PervasiveHealth 2025), LNICST vol. 693 (2026) · Jul 2, 2026
Talking to Avatars: How Human-Like Should Virtual Health Assistants Be in Medical History Collection?
Ying Chen, Amirreza Gholami, Jun Hu
In plain English
Twenty-seven participants gave a medical history to a GPT-powered assistant rendered three ways: abstract, stylised and realistic. More human-like avatars drew better-quality answers and better-rated experiences, but the authors report that emotional comfort did not come from realism itself — it came from appearance, behaviour and the user's sense of what the agent is being consistent with each other. In practice that favours a stylised avatar that behaves coherently over a photoreal one that does not, particularly when the conversation is about something sensitive.
Abstract
Virtual health agents are gaining attention in medical contexts, where their anthropomorphic features may influence users' emotional responses and willingness to share personal information. This paper investigates how different levels of avatar anthropomorphism—abstract, stylized, and realistic—shape user expression and experience in AI-assisted medical history collection. We developed a GPT-powered conversational system and conducted a within-subject experiment (N = 27) combining standardized questionnaires and semi-structured interviews. Results show that perceived anthropomorphism was positively associated with both response quality and user experience. However, emotional comfort arises not from realism per se, but from the alignment between visual appearance, behavioral coherence, and users' social framing of the agent. We contribute actionable design implications for creating trustworthy, empathetic digital health assistants, and refine theoretical understandings of anthropomorphism in sensitive health communication.
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