Achieving trust in health-behavior-change artificial intelligence apps (HBC-AIApp) development: A multi-perspective...

Area of Science:

• Digital Health
• Human-Computer Interaction
• Artificial Intelligence in Healthcare

Background:

• Trust is a critical factor for the adoption and effectiveness of Health-Behavior-Change Artificial Intelligence Apps (HBC-AIApp).
• Existing development methods often lack specific guidance for fostering user trust in these complex systems.
• Developers require theory-based, practical approaches to build trustworthy HBC-AIApps.

Purpose of the Study:

• To develop a comprehensive conceptual model for trust in HBC-AIApps.
• To create an extended development process (IDEAS) to guide the creation of trustworthy HBC-AIApps.
• To provide a framework that integrates user-centered design, medical informatics, and holistic health principles.

Main Methods:

• A multi-disciplinary approach integrating medical informatics, human-centered design, and holistic health.
• Extension of a conceptual model of AI trust (Jermutus et al.).
• Adaptation of the IDEAS (integrate, design, assess, and share) development process.

Main Results:

• A three-block framework for HBC-AIApp development was established, encompassing user reality, stakeholder/mediator roles, and app structural components.
• An extended conceptual model of trust in HBC-AIApps was formulated.
• An enhanced IDEAS development process was proposed, incorporating trust-building elements.

Conclusions:

• The developed framework and IDEAS process offer a structured approach for developers to build user trust in HBC-AIApps.
• The framework integrates user perceptions, needs, and environment with AI logic and implementation.
• Further research is needed to validate the effectiveness of this framework in real-world applications.