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

Artificial Intelligence (668) Decision Support for Health Professionals (737)

Published on in Vol 21 , No 10 (2019) :October

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

Authors of this article:

Tiantian Ye 1, 2 Author Orcid Image ;   Jiaolong Xue 3, 4 Author Orcid Image ;   Mingguang He 5, 6 Author Orcid Image ;   Jing Gu 7 Author Orcid Image ;   Haotian Lin 6 Author Orcid Image ;   Bin Xu 8 Author Orcid Image ;   Yu Cheng 1, 9 Author Orcid Image
Article Authors Cited by (32) Tweetations (9) Metrics

Journals

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  4. Tsai W, Wu Y, Cheng C, Kuo M, Chang Y, Hu F, Sun C, Chang C, Chan T, Chen C, Lee C, Chu C. A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study. Journal of Medical Internet Research 2021;23(4):e27069 View
  5. Huang Y, Trinh M, Le T. Critical Factors Affecting Intention of Use of Augmented Hearing Protection Technology in Construction. Journal of Construction Engineering and Management 2021;147(8) View
  6. Zhai H, Yang X, Xue J, Lavender C, Ye T, Li J, Xu L, Lin L, Cao W, Sun Y. Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study. Journal of Medical Internet Research 2021;23(9):e27122 View
  7. Zhang X, Zhang R. Impact of Physicians’ Competence and Warmth on Chronic Patients’ Intention to Use Online Health Communities. Healthcare 2021;9(8):957 View
  8. Loh H, Hong W, Ooi C, Chakraborty S, Barua P, Deo R, Soar J, Palmer E, Acharya U. Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021). Sensors 2021;21(21):7034 View
  9. Calisto F, Nunes N, Nascimento J. Modeling adoption of intelligent agents in medical imaging. International Journal of Human-Computer Studies 2022;168:102922 View
  10. Young A, Amara D, Bhattacharya A, Wei M. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. The Lancet Digital Health 2021;3(9):e599 View
  11. Mukherjee J. Adoption of personal service robots in India. IIMB Management Review 2022;34(4):378 View
  12. Yap A, Wilkinson B, Chen E, Han L, Vaghefi E, Galloway C, Squirrell D. Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening. Asia-Pacific Journal of Ophthalmology 2022;11(3):287 View
  13. Chen J, Li T, You H, Wang J, Peng X, Chen B. Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study. International Journal of Environmental Research and Public Health 2023;20(4):3247 View
  14. Knop M, Weber S, Mueller M, Niehaves B. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review. JMIR Human Factors 2022;9(1):e28639 View
  15. Kelly S, Kaye S, Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics 2023;77:101925 View
  16. Zhu Y, Lu Y, Gupta S, Wang J, Hu P. Promoting smart wearable devices in the health-AI market: the role of health consciousness and privacy protection. Journal of Research in Interactive Marketing 2023;17(2):257 View
  17. Kohne Z, Shahrestanaki S, Parvizy S, Shoghi M. Concept analysis of adoption: A hybrid model. Journal of Child and Adolescent Psychiatric Nursing 2023 View
  18. Xiong J, Sun D, Wang Y. Adoption of artificial intelligence artifacts: a literature review. Universal Access in the Information Society 2023 View
  19. Galetsi P, Katsaliaki K, Kumar S, Ferguson M. What affects consumer behavior in mobile health professional diagnosis applications. Decision Sciences 2021 View
  20. Lünich M, Kieslich K. Exploring the roles of trust and social group preference on the legitimacy of algorithmic decision-making vs. human decision-making for allocating COVID-19 vaccinations. AI & SOCIETY 2022 View
  21. Ng W, Zhang S, Wang Z, Ong C, Gunasekeran D, Lim G, Zheng F, Tan S, Tan G, Rim T, Schmetterer L, Ting D. Updates in deep learning research in ophthalmology. Clinical Science 2021;135(20):2357 View
  22. Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. Advances in Ophthalmology Practice and Research 2022;2(3):100078 View
  23. Ismatullaev U, Kim S. Review of the Factors Affecting Acceptance of AI-Infused Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 2022:001872082110647 View
  24. Khanijahani A, Iezadi S, Dudley S, Goettler M, Kroetsch P, Wise J. Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review. Health Policy and Technology 2022;11(1):100602 View
  25. Tseng R, Gunasekeran D, Tan S, Rim T, Lum E, Tan G, Wong T, Tham Y. Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers’ and Patients’ Perspectives. Asia-Pacific Journal of Ophthalmology 2021;10(3):299 View
  26. Del Giudice M, Scuotto V, Orlando B, Mustilli M. Toward the human – Centered approach. A revised model of individual acceptance of AI. Human Resource Management Review 2023;33(1):100856 View
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Books/Policy Documents

  1. Jain V, Singh N, Pradhan S, Gupta P. Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. View
  2. Kim J, Jung H, Park M, Lee S, Lee H, Kim Y, Nan D. HCI International 2022 Posters. View