Previously submitted to: JMIR AI (no longer under consideration since Apr 08, 2026)
Date Submitted: Mar 31, 2026
Open Peer Review Period: Apr 8, 2026 - Jun 3, 2026
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Enhancing Human-Centered Communication Design in AI Health Conversational Agents
ABSTRACT
This letter responds to Qin et al’s systematic review and meta-analysis on communication competence in AI conversational agents (CAs) for health. While the review demonstrates that communication strategies—such as empathy, personalization, contingency, explanation, and partnership—significantly improve user evaluations and psychological outcomes, we argue that further conceptual and empirical refinement is needed. First, communication competence should be examined as a context-sensitive construct shaped by health literacy, language proficiency, and cultural expectations, rather than as a universally effective design feature. Second, the observed gap between improved user-centered outcomes and nonsignificant health outcomes suggests that relational quality alone may be insufficient to produce measurable clinical benefits without integration into broader care pathways and behavior-change supports. Third, transparency—including clear disclosure of system capabilities, limitations, and uncertainty—should be treated as a core dimension of communication competence to calibrate trust and prevent overreliance. Advancing research in these areas will help ensure that human-centered communication design in health CAs is not only engaging, but also equitable, clinically meaningful, and safe.
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