ArticleFrontiers in public health2025
Factors influencing the acceptance of medical AI chat assistants among healthcare professionals and patients: a survey-based study in China.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Effects of a Safety User Interface Bundle on Verification Intentions in Generative AI Chat Use Among Older Chinese Adults: Randomized Vignette Survey.Journal of medical Internet research · 2026Trial
- Effects of Performance and Effort Expectancy on AI-Generated Information Adoption Among Chinese Nursing Professionals: Survey-Based SEM Analysis.Journal of advanced nursing · 2026Article
- Eye-Tracked Visual Attention to Anthropomorphic Appearance and Empathic Responses in AI Medical Conversational Agents: Dissociating Trust Gains from Attentional Synergy.Journal of eye movement research · 2026Article
- Acceptance of Medical History-Taking Supported by Artificial Intelligence and Chatbots: A Population-Based Survey in Germany.Healthcare (Basel, Switzerland) · 2026Article
- New eHealth Users After COVID-19: Adoption and Resistance in Spain.Healthcare (Basel, Switzerland) · 2026Article
- Bridging perception and reality: understanding Chinese women's behavioral intentions toward robot-assisted gynecologic surgery.Frontiers in public health · 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
- Trusting and staying with AI doctors: cognitive and emotional pathways driving continuance intention toward AI healthcare chatbots.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Introduction: This study examines the drivers behind healthcare professionals' and patients' acceptance of medical AI chat assistants in China. Methods: A nationwide online survey was conducted from March 10 to April 28, 2024, using quota sampling to collect 500 valid responses, and data were analyzed via structural equation modeling. Results: Performance expectancy, perceived cost, digital access, and digital competence all positively predicted intention to use, whereas patient age and consultation frequency-rather than socioeconomic status-directly influenced actual usage behavior. Discussion: Findings validate an extended UTAUT model in digital-health contexts, highlight cultural moderators of technology acceptance, and inform age-friendly AI design and equitable health-informatization policies in aging societies.
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