ArticleDigital health
Promoting trust and intention to adopt health information generated by ChatGPT among healthcare customers: An empirical study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Consumer and Patient Health Information Seeking With Generative AI Tools: Scoping Review of Facilitators and Barriers.Journal of medical Internet research · 2026Article
- Channel Effects on Online Health Information Seeking in the Age of AI: An Extension of the CMIS Framework.Behavioral sciences (Basel, Switzerland) · 2026Article
- Acceptance and use of GenAI among medical and health sciences students in Saudi Arabia: an extended TAM study.BMC medical education · 2026Article
- Public awareness, trust, perceived usefulness, and willingness to use ChatGPT for healthcare communication among adults in the Asir Region, Saudi Arabia: a cross-sectional study.Frontiers in public health · 2026Article
- Why do I use generative artificial intelligence (GenAI) to seek health information? A perceptual perspective of GenAI users.Frontiers in public health · 2026Article
- Modeling Behavioral Determinants of Following and Verifying AI-Generated Health Advice: The Roles of eHealth Literacy, Cognitive Load, and Technology Self-Efficacy.Journal of multidisciplinary healthcare · 2026Article
- Acceptance of generative AI-assisted medical decision-making among Chinese physicians and patients and its ethical determinants: a cross-sectional survey.Frontiers in public health · 2026Article
- Psychological factors associated with Chinese users' adoption of AI-generated health education short videos: a PLS-SEM-ANN-IPMA study.Frontiers in psychology · 2026Article
- Public trust in AI-enabled telemedicine: affective, cognitive, and structural dimensions insights from multi-platform big data analytics.Frontiers in medical technology · 2026Article
- From digital assistants to clinical partners: revolutionizing pediatric urology through large language model-powered decision support and patient education.World journal of urology · 2025Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: As a representative product of generative artificial intelligence (GenAI), ChatGPT demonstrates significant potential to enhance healthcare outcomes and improve the quality of life for healthcare consumers. However, current research has not yet quantitatively analysed trust-related issues from both the healthcare consumer perspective and the uncertainty perspective of human-computer interaction. Objective: This study aims to analyse the antecedents of healthcare consumers' trust in ChatGPT and their adoption intentions towards ChatGPT-generated health information from the perspective of uncertainty reduction. Methods: An anonymous online survey was conducted with healthcare customers in China between September and October 2024. This survey included questions on critical constructs such as social influence, situational normality, anthropomorphism, autonomy, personalisation, information quality, information disclosure, trust in ChatGPT, and intention to adopt health information. A 7-point Likert scale was used to score each item, ranging from 1 (strongly disagree) to 7 (strongly agree). SmartPLS 4.0 was used to analyse data and test the proposed theoretical model. Results: The findings indicated that trust in ChatGPT had a significant relationship with the intention to adopt health information. The primary factors associated with trust in ChatGPT and the intention to adopt health information were social influence, situational normality, autonomy, personalisation, and information quality. The analysis revealed a negative relationship between social influence and trust in ChatGPT. Familiarity with ChatGPT was identified as a significant control variable. Conclusion: Trust in ChatGPT is positively related to healthcare consumers' adoption of health information, with information quality as a key predictor. The findings offer empirical support and practical guidance for enhancing trust and encouraging the use of GenAI-generated health information.
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