Accessibility settings

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

Clinical Information and Decision Making (2138) Adoption and Change Management of eHealth Systems (980) Decision Support for Health Professionals (1686) Methods and Instruments in Medical Informatics (225) User Needs and Competencies (512) E-Health / Health Services Research and New Models of Care (823) Artificial Intelligence (2441) Computer-Aided Diagnosis (285)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62732, first published .
Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

Authors of this article:

Rui Zheng1 Author Orcid Image ;   Xiao Jiang2 Author Orcid Image ;   Li Shen3 Author Orcid Image ;   Tianrui He4 Author Orcid Image ;   Mengting Ji5 Author Orcid Image ;   Xingyi Li6 Author Orcid Image ;   Guangjun Yu1 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

Journals

  1. Yamamoto M, Kataoka Y, Tsujimoto Y, Nagase F, Iwasaki K, Ito Y, Ikai H, Watanabe T, Yokoyama-Kokuryo W, Takizawa N, Fujita Y. Disease Activity Assessment Frequency in Rheumatoid Arthritis: A Retrospective Observational Study of the Medical Support System for Rheumatoid Arthritis System Implementation. JMIR Formative Research 2025;9:e74222 View
  2. Parsons C, Zuiderwijk A, Orchard N, Oosterhoff J, de Reuver M. Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies. BMC Medical Informatics and Decision Making 2025;25(1) View
  3. Thanthrige A, Lu B, Sako Z, Wickramasinghe N. Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e64524 View
  4. Salazar L, Rivera L. Model of Acceptance of Artificial Intelligence Devices in Higher Education. Computers 2026;15(1):46 View
  5. Shao H, Zhu S, Zhang C, Ji H, Li W, Zhang J, Fu W. Determinants of healthcare professionals’ acceptance of DeepSeek-assisted clinical decision support applications: a cross-sectional study. BMC Medical Informatics and Decision Making 2026;26(1) View
  6. Borat S, Chowdhury S. Artificial intelligence-driven clinical decision support systems for precision oncology: A comprehensive review. In Silico Research in Biomedicine 2026;2:100265 View
  7. Shi H, Xiang Y, Yang X, Zhao Q, Huang H. Identifying Measurement Dimensions of Users’ Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2026;63 View
  8. Hasan K, Akhtar M, Hasnat M, Khandakar H. Ethical AI innovation in healthcare and sustainable development in Bangladesh. Journal of Health Organization and Management 2026:1 View
  9. Ur Rahman S, Nadir K, Ilyas M, Nigar M, Khan A, Rahman A, Hussain S. Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation. medtigo Journal of Medicine 2026 View
  10. Chen H, Yang X, Peng J, Xu X, Li Y, Tong Z, Gao C. Development and Application of a Unplanned Extubation Assessment and Clinical Decision Support Information System. Risk Management and Healthcare Policy 2026;Volume 19:1 View
  11. Daly J, Delen D, Han Z, Smith R, Honerlaw J, Cho K, Bennett B, Sippel J. AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges. Journal of Medical Internet Research 2026;28:e71532 View
  12. 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
  13. Shi H, Wang H, Zheng S, Xiao W, Ju M, Zhao Q, Huang H. How healthcare professionals perceive artificial intelligence risks: A grounded theory exploration of antecedents, dimensions, and outcomes. DIGITAL HEALTH 2026;12 View

Conference Proceedings

  1. Fahlevi E, Suryantoro R, Sahlan M, Tamara D. 2025 International Conference on Information Management and Technology (ICIMTech). Extending the Technology Acceptance Model: Investigating Fintech Adoption Among SMEs in Indonesia View