ArticleJournal of medical Internet research2019
Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Artificial intelligence adoption in healthcare: a systematic review of implementation challenges and health services implications.BMC health services research · 2026Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.Journal of medical Internet research · 2024Pooled it
- Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey Experiment.Journal of medical Internet research · 2025Trial
- Toward tailored implementation of an artificial intelligence-based healthy dietary chatbot: differences in technology acceptance pathways by health consciousness and user characteristics.BMC medical informatics and decision making · 2026Article
- Cross-cultural determinants of AI acceptance in medical education among medical students in China and Pakistan.BMC medical education · 2026Article
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- The role of AI-mediated learning contexts in the personality-self-directed learning relationship among Chinese medical undergraduates: a cross-sectional study.BMC medical education · 2026Article
- Medical Practitioners' Acceptance and Use of AI-Based Clinical Decision Support Systems in Western China: A Mixed-Methods Study.Healthcare (Basel, Switzerland) · 2026Article
- Factors influencing user adoption of AI painting tools based on the AIDUA model.Scientific reports · 2026Article
- Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model Integrating Trust and Risk.Healthcare (Basel, Switzerland) · 2026Article
- A study on factors influencing music enthusiast's behavioral intention to use generative AI for music composition based on an expanded technology acceptance model.Scientific reports · 2025Article
- An empirical study on the use behavior towards AI painting tools based on TAM3 model.Scientific reports · 2025Article
- Structural equation modeling for influencing factors on behavioral intention to adopt medical AI among Chinese nurses: a nationwide cross-sectional study.BMC nursing · 2025Article
- Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.Eye (London, England) · 2025Review
- Article
- Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education.NPJ science of learning · 2025Article
- Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis.Journal of medical Internet research · 2025Article
- EfficientNetB0-Based End-to-End Diagnostic System for Diabetic Retinopathy Grading and Macular Edema Detection.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field.
objectiveThis study aimed to investigate the general public's acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people's intention to use these devices.
methodsWe proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care-related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model's path effects, significance, goodness of fit, and mediation and moderation effects were analyzed.
resultsStandardized factor loadings of items were between 0.583 and 0.876. Composite reliability of 9 constructs ranged from 0.673 to 0.841. The discriminant validity of all constructs met the Fornell and Larcker criteria. Model fit indicators such as standardized root mean square residual (0.057), comparative fit index (0.915), and root mean squared error of approximation (0.049) demonstrated good fit. Intention to use (R
conclusionsThe item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.