Study on Determinants of Trust, Data Sharing Attitudes, and User Preferences Toward AI-Based Dietary Recommendation Systems

This research examines the integration of artificial intelligence (AI) in the healthcare industry, with specific reference to AI-based dietary recommendation systems. This research seeks to establish the key factors that affect user trust, attitudes toward information sharing, and user preferences. A questionnaire-based research method is adopted to collect data from the participants, who are asked about their engagement with mobile health applications, their willingness to share information, their level of trust in traditional dietitians, and their perceptions about AI-based dietary recommendation systems. The research results show that how familiar users are with diet application software differs. Trends suggest that familiarity may be linked to how often people use the application. However, this connection is mostly examined through correlation analysis and not backed by more thorough methods like regression. Also, while more frequent application usage seems to relate to greater user trust in AI-based dietary recommendation systems, this conclusion relies on correlation findings, not solid causal evidence. Although the participants showed comfort with information sharing, they showed reluctance in sharing information about their dietary history. The results show that the level of trust in traditional dietitians is relatively high, indicating the significant role played by traditional dietitians in dietary recommendation. The results show that the cost-effectiveness of AI-based dietary recommendation systems is an important motivational factor, with the majority showing unwillingness to adopt AI-based dietary recommendation systems without professional counselling, even if the costs are reduced. Moreover, the results show that the participants show a preference for flexible dietary plans over rigid dietary plans.

Artificial Intelligence, Dietary Recommendation Systems

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