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This paper is in the following e-collection/theme issue:

Artificial Intelligence (2480) Adoption and Change Management of eHealth Systems (995) Ethical, Legal, and Social Issues in AI (139)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75343, first published .
Doctor in scrubs touching holographic medical displays of heart, brain, and DNA

Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study

Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study

Authors of this article:

Qianqian Dai1 Author Orcid Image ;   Ming Li2 Author Orcid Image ;   Maoshu Yang3 Author Orcid Image ;   Shiwu Shi3 Author Orcid Image ;   Zhaoyu Wang4 Author Orcid Image ;   Jiaojiao Liao4 Author Orcid Image ;   Zhaoji Li4 Author Orcid Image ;   Weinan E1, 5 Author Orcid Image ;   Liyuan Tao1, 4 Author Orcid Image ;   Yi-Da Tang1, 6 Author Orcid Image
Article Authors Cited by (17) Tweetations Metrics

Journals

  1. Li C, Tsuei S, Wu H. AI Literacy Among Chinese Medical Students: Cross-Sectional Examination of Individual and Environmental Factors. JMIR Medical Education 2026;12:e80604 View
  2. Khan S. Modeling enablers of artificial intelligence adoption in smart hospitals: An ISM–MICMAC analysis. Intelligent Hospital 2026;2(1):100049 View
  3. Giebel G, Raszke P, Tokic M, Palmowski L, Timmesfeld N, Nowak H, Adamzik M, Heinz P, Mreyen S, Brunkhorst F, Wasem J, Buchner F, Blase N. Attitude and perception toward artificial intelligence among German physicians with intensive care experience: a survey study. Frontiers in Health Services 2026;5 View
  4. Ge X, Wang H, Xu Y, Zhang Y. Knowledge and attitudes regarding AI-assisted documentation among clinical nurses in China: a cross-sectional study. BMC Nursing 2026;25(1) View
  5. Al-Mutawah M. Mathematics teachers’ perceptions of artificial intelligence in education (AIED): Practices and challenges. Social Sciences & Humanities Open 2026;13:102579 View
  6. Lin A, Zeng M, Gan W, Jiang A, Liu Y, Qi C, Zhu L, Mou W, Zeng D, Xiao M, Chu G, Peng S, Wong H, Zhang L, Zhang H, Deng X, Zhang J, Cheng Q, Tang B, Luo P. Medicine digital transformation: evidence from Chinese physicians on generative artificial intelligence implementation and challenges. Journal of Translational Medicine 2026;24(1) View
  7. Bold B, Tokuda B, Kashif M, Fadzli F, Wee N, Kanwal U, Nguyễn T, Wah N. Radiologists’ perceived value and readiness for artificial intelligence in value-based radiology: a multicountry survey. Japanese Journal of Radiology 2026 View
  8. Clej E, Mavrea A, Fizedean C, Tănase A, Ilie A, Tischer A. How Digital Stress and eHealth Literacy Relate to Missed Nursing Care and Willingness to Use AI Decision Support. Healthcare 2026;14(8):996 View
  9. Li X, Xu H, Hu X, Guo J, Yu P, Ju H. Heterogeneity in nurses’ attitudes toward artificial intelligence: a latent profile analysis. BMC Health Services Research 2026;26(1) View
  10. Xie Y, Song Z, Wang E, Li P, Wang J. Beyond willingness: unpacking pharmacists’ adoption of AI-driven clinical decision support systems through an extended UTAUT framework. Frontiers in Public Health 2026;14 View
  11. Du X, Ling C, Yang L, Wei L. Factors shaping the adoption of large language models among hospital administrative staff: A cross-sectional survey study. DIGITAL HEALTH 2026;12 View
  12. Shen L. Investigating Cognition, Attitudes, and Acceptance of Smart Elderly Care Systems among Chinese Nurses. HUMAN BEHAVIOR, DEVELOPMENT and SOCIETY 2026;27(2):283554 View
  13. Kranz A, Schulz A, Abele H, Graf J. Competency Goals in Midwifery Master’s Programs in Germany and Selected OECD Countries: Comparison of Stakeholder Perspectives. Healthcare 2026;14(10):1377 View
  14. Zhang A, Sheng A, Chen H, Ning H, Li R, Yu Z. Adopting AI in medical ethics review: A configurational fsQCA study of practitioners’ willingness. DIGITAL HEALTH 2026;12 View
  15. Sagong H, Feeley C. Development and Validation of the AI Health Acceptability Evaluation (AI-HAE) in Older Adults. CIN: Computers, Informatics, Nursing 2026 View
  16. Alqasim S, Bindakhil F, Alzahrani M, Alhajri S, Alzaidi G, Alhajri S, Tashkandi N, Arafah A. AI-powered tools in family medicine: Bridging technology and practice. Journal of Family Medicine and Primary Care 2026;15(4):1713 View
  17. Hermansson-Borrebaeck R, Milos-Nymberg V, Glock H, Jakobsson U, Midlöv P, Calling S. Factors affecting nurses’ acceptance of a digital triage platform in primary health care in Sweden: an extended UTAUT analysis. Scandinavian Journal of Primary Health Care 2026;44(1) View