Overview
The rapid rise of generative artificial intelligence (AI) has transformed how people seek health information. Beyond traditional sources like physicians, pharmacists, and search engines, individuals now encounter tools such as chatbots and AI-powered assistants. This study compares how different populations predict their intention to use generative AI for health information, offering insights for healthcare providers, technology developers, and policymakers about adoption drivers and perceived barriers.
The Conceptual Framework
Intention to use health-related AI tools is shaped by multiple factors including perceived usefulness, perceived ease of use, trust, privacy concerns, and prior experience with digital health technologies. The comparative survey examined these dimensions across diverse groups to identify similarities and differences in adoption readiness. By linking theory to empirical data, the study aims to illuminate how users weigh benefits against risks when considering AI-based health information sources.
Methods in Brief
Researchers conducted a cross-sectional survey with multiple cohorts representing age, education, and digital literacy levels. Respondents answered validated scales measuring attitudes toward generative AI, prior use of health apps, trust in AI, and anticipated likelihood of seeking health information from AI assistants. The study employed regression analyses and comparative statistics to uncover which factors most strongly predict intention to use across groups.
Key Findings
Across cohorts, perceived usefulness emerged as a consistent driver of intention to use generative AI for health information. Participants who believed AI could provide accurate, timely, and personalized responses were more inclined to try these tools. Ease of use and straightforward interfaces also boosted willingness, particularly among individuals with limited digital experience.
Trust played a critical moderating role. Higher trust in AI systems and in the organizations behind them correlated with greater intention to engage with AI health information. Conversely, privacy concerns and fears about misinformation reduced readiness in several groups, highlighting the importance of transparent data practices and robust safeguards.
Demographic nuances mattered. Younger respondents tended to report higher intention to use AI tools, while older adults emphasized reliability and doctor-supervised usage. Education level influenced comfort with complex AI features, with more educated respondents showing greater openness to experimentation when guided by credible sources.
Notably, prior experience with digital health technologies predicted higher intention to adopt generative AI. Familiarity with health apps, online symptom checkers, and telehealth services created a foundation that facilitated acceptance of AI-driven health information.
<h2Implications for Practice
For healthcare providers, acknowledging patient interest in AI can improve patient education and shared decision-making. Clinicians should discuss AI recommendations as supplementary tools, clarify limitations, and provide guidance on identifying trustworthy AI sources. For developers, the findings suggest prioritizing user-friendly design, transparent data handling, and clear explanations of AI reasoning to build trust and reduce perceived risk.
Policymakers can support responsible AI adoption by establishing standards for privacy, accuracy, and accountability. Public health campaigns may emphasize how to evaluate AI health information critically, reducing the spread of misinformation while encouraging beneficial use cases.
<h2Limitations and Future Work
As a cross-sectional survey, the study captures attitudes at a single point in time. Longitudinal research could reveal how intentions translate into sustained usage and health outcomes. Future work might explore cultural differences, the impact of clinical oversight on adoption, and the effectiveness of educational interventions designed to boost technological literacy in health contexts.
<h2Conclusion
Understanding the intention to use generative AI for health information is essential as patients increasingly navigate AI-supported health decisions. By identifying key drivers and barriers, stakeholders can tailor approaches that maximize usefulness, protect privacy, and foster trust—ultimately helping individuals access accurate health information in an evolving digital landscape.
