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

Journals

  1. Chang C. Exploring the Usage Intentions of Wearable Medical Devices: A Demonstration Study. Interactive Journal of Medical Research 2020;9(3):e19776 View
  2. Javaid M, Haleem A, Khan I, Vaishya R, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. Apollo Medicine 2020;17(1):53 View
  3. Qi M, Cui J, Li X, Han Y. Perceived Factors Influencing the Public Intention to Use E-Consultation: Analysis of Web-Based Survey Data. Journal of Medical Internet Research 2021;23(1):e21834 View
  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;36(2):155 View
  18. Xiong J, Sun D, Wang Y. Adoption of artificial intelligence artifacts: a literature review. Universal Access in the Information Society 2024;23(2):703 View
  19. Galetsi P, Katsaliaki K, Kumar S, Ferguson M. What affects consumer behavior in mobile health professional diagnosis applications. Decision Sciences 2023;54(3):315 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 2024;39(1):309 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 2024;66(1):126 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
  27. Chaibi A, Zaiem I. Doctor Resistance of Artificial Intelligence in Healthcare. International Journal of Healthcare Information Systems and Informatics 2022;17(1):1 View
  28. Zhang X, Wang J, Hao Y, Wu K, Jiao M, Liang L, Gao L, Ning N, Kang Z, Shan L, He W, Wang Y, Wu Q, Yin W. Prevalence and Factors Associated With Burnout of Frontline Healthcare Workers in Fighting Against the COVID-19 Pandemic: Evidence From China. Frontiers in Psychology 2021;12 View
  29. Nurek M, Kostopoulou O. How the UK public views the use of diagnostic decision aids by physicians: a vignette-based experiment. Journal of the American Medical Informatics Association 2023;30(5):888 View
  30. Degefe E, Prabowo Y, Savani K, Sheetal A. Functional Analogies Increase Trust in Black-Box AI Systems Among Lay Consumers: The Case of GeNose C-19. Computer 2023;56(5):74 View
  31. Hameed B, Naik N, Ibrahim S, Tatkar N, Shah M, Prasad D, Hegde P, Chlosta P, Rai B, Somani B. Breaking Barriers: Unveiling Factors Influencing the Adoption of Artificial Intelligence by Healthcare Providers. Big Data and Cognitive Computing 2023;7(2):105 View
  32. Vo V, Chen G, Aquino Y, Carter S, Do Q, Woode M. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Social Science & Medicine 2023;338:116357 View
  33. Deng Z, Tian Z, Xue J, Gupta S. What predicts patients’ satisfaction and continuous use of intelligent medical guidance? the moderating effect of consulting experience. Behaviour & Information Technology 2023:1 View
  34. Xu J, Zhang X, Li H, Yoo C, Pan Y. Is Everyone an Artist? A Study on User Experience of AI-Based Painting System. Applied Sciences 2023;13(11):6496 View
  35. Li L, Haley L, Boyd A, Bernstam E. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. Journal of Biomedical Informatics 2023;147:104531 View
  36. Sonntag M, Mehmann J, Teuteberg F. Trust-Supporting Design Elements as Signals for AI-Based Chatbots in Customer Service. International Journal of Service Science, Management, Engineering, and Technology 2023;14(1):1 View
  37. Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. Journal of Medical Internet Research 2023;25:e48249 View
  38. Willis K, Chaudhry U, Chandrasekaran L, Wahlich C, Olvera-Barrios A, Chambers R, Bolter L, Anderson J, Barman S, Fajtl J, Welikala R, Egan C, Tufail A, Owen C, Rudnicka A. What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting. BMJ Open 2023;13(11):e075558 View
  39. Cai Z, He H, Huo W, Xu X. More Unique, More Accepting? Integrating Sense of Uniqueness, Perceived Knowledge, and Perceived Empathy with Acceptance of Medical Artificial Intelligence. International Journal of Human–Computer Interaction 2023:1 View
  40. Ngo V. Does ChatGPT change artificial intelligence-enabled marketing capability? Social media investigation of public sentiment and usage. Global Media and China 2024;9(1):101 View
  41. Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clinics in Dermatology 2024;42(3):210 View
  42. Oprea S, Nica I, Bâra A, Georgescu I. Are skepticism and moderation dominating attitudes toward AI‐based technologies?. The American Journal of Economics and Sociology 2024;83(3):567 View
  43. Goh W, Chia K, Cheung M, Kee K, Lwin M, Schulz P, Chen M, Wu K, Ng S, Lui R, Ang T, Yeoh K, Chiu H, Wu D, Sung J. Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study. JMIR AI 2024;3:e50525 View
  44. Frost E, Bosward R, Aquino Y, Braunack-Mayer A, Carter S. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. International Journal of Medical Informatics 2024;186:105417 View
  45. Giavina-Bianchi M, Amaro Jr E, Machado B. Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study. JMIRx Med 2024;5:e50803 View
  46. Yang Y, Ngai E, Wang L. Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda. Information & Management 2024;61(4):103961 View
  47. Zou X, Na Y, Lai K, Liu G. Unpacking public resistance to health Chatbots: a parallel mediation analysis. Frontiers in Psychology 2024;15 View
  48. Jain G, Pareek S, Carlbring P. Revealing the source: How awareness alters perceptions of AI and human-generated mental health responses. Internet Interventions 2024;36:100745 View
  49. Pan Z, Xie Z, Liu T, Xia T. Exploring the Key Factors Influencing College Students’ Willingness to Use AI Coding Assistant Tools: An Expanded Technology Acceptance Model. Systems 2024;12(5):176 View
  50. Hassan M, Kushniruk A, Borycki E. Barriers and Facilitators of Artificial Intelligence Adoption in Healthcare: A Scoping Review (Preprint). JMIR Human Factors 2023 View
  51. Zhang D, Zhao X. Understanding adoption intention of virtual medical consultation systems: Perceptions of ChatGPT and satisfaction with doctors. Computers in Human Behavior 2024;159:108359 View

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
  3. Yousra M, Khalid C. International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). View
  4. Chauhan A, Gulati C, Mathur G, Sankpal S. Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry. View
  5. Sadriwala M, Dadhich M. The AI Revolution: Driving Business Innovation and Research. View