Exploring the influence of privacy concerns, AI literacy, and perceived health stigma on AI chatbot use in healthcare: An uncertainty reduction approach.

OBJECTIVE

Guided by Uncertainty Reduction Theory, this study aims to explore the influence of AI privacy concerns, AI literacy, and perceived health stigma on the preference for AI chatbots in Chinese healthcare contexts.

METHOD

We analyzed survey data from 1487 participants, employing frequency analyses to generate descriptive statistics and regression modeling to examine the relationships among key variables.

RESULTS

The findings indicate that AI chatbots are most highly valued for their roles in health information dissemination and lifestyle guidance. Privacy concerns negatively affect perceptions of AI as a substitute for doctors, particularly regarding sensitive health issues. However, AI literacy mitigates these concerns across all functional domains. Additionally, perceived health stigma enhances the acceptance of AI chatbots as substitutes for doctors in sensitive areas and intensifies the adverse impact of privacy concerns on AI's role in health information, decision-making, and lifestyle guidance.

CONCLUSION

The current study yields valuable insights into the role of AI chatbots in healthcare contexts, particularly in shaping users' intentions to disclose personal information. It also advances the theoretical scope of Uncertainty Reduction Theory by extending its application to human-AI interactions.

PRACTICAL IMPLICATIONS

The findings underscore the need for healthcare providers, policymakers, and AI developers to prioritize privacy safeguards, promote AI literacy, and actively combat stigma. Such efforts are essential to fostering trust and encouraging meaningful patient engagement with AI-driven tools, especially in addressing sensitive health concerns.