Use and Efficacy of Conversational AI in Health Management: An Integrative Review

Area of Science:

• Health Communication
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Conversational artificial intelligence (AI) is increasingly used in health communication.
• Existing research often overlooks communication processes, focusing instead on usability or technical performance.
• This review addresses the gap by analyzing AI's role in health interactions from a communication perspective.

Purpose of the Study:

• To comprehensively analyze empirical research on conversational AI in health communication.
• To identify the communication roles and mechanisms of AI in health interactions.
• To develop a framework explaining AI's impact on health communication outcomes.

Main Methods:

• A theory-oriented integrative review of 65 empirical studies.
• Thematic synthesis to identify core communication roles of conversational AI.
• Development of the Conversational AI-Mediated Health Communication (CA-MHC) framework.

Main Results:

• Identified four core communication roles for conversational AI: information communicator, behavioral interventionist, emotional supporter, and service mediator.
• Found significant variations in communication outcomes across different contexts.
• Developed the CA-MHC framework, conceptualizing AI as a mediated agent whose impact depends on role-configuration fit.

Conclusions:

• Conversational AI can support or hinder effective health communication based on its role and communication configuration.
• The CA-MHC framework offers a conceptual foundation for understanding AI's influence in health interactions.
• Provides guidance for future theoretical development and empirical research in AI-mediated health communication.