Predicting the Intention to Use Generative Artificial Intelligence for Health Information: Comparative Survey Study.

Area of Science:

• Digital Health
• Artificial Intelligence
• Health Communication

Background:

• Generative artificial intelligence (AI) tools are transforming online information access.
• In healthcare, generative AI presents a disruptive potential due to accessibility and tailored content.
• Questions remain regarding generative AI's health information usage and its impact on digital health equity.

Purpose of the Study:

• Assess the relevance of generative AI as a health information source.
• Identify factors predicting the intention to use generative AI for health.
• Compare adoption factors across four European countries.

Main Methods:

• Online survey of 1990 participants aged 16-74 across Austria, Denmark, France, and Serbia.
• Applied the Unified Theory of Acceptance and Use of Technology 2, extended with health literacy and health status.
• Structural equation modeling with metric measurement invariance tested cross-national associations.

Main Results:

• Generative AI health information usage was limited (39.5% rarely used).
• Medical experts and search engines remain primary health information sources.
• Performance expectancy, habit, and hedonic motivation significantly predicted intention to use generative AI for health across all countries.

Conclusions:

• Current generative AI adoption for health is driven by early adopters finding it useful, convenient, and enjoyable.
• Consistent cross-national adoption patterns suggest a shared European trend.
• Promoting equitable adoption requires focusing on usefulness, convenience, enjoyment, digital access, and safeguards for vulnerable users.