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

Clinical Information and Decision Making (1953) Artificial Intelligence (2185) Decision Support for Health Professionals (1563) Adoption and Change Management of eHealth Systems (911) E-Health / Health Services Research and New Models of Care (733) Methods and Instruments in Medical Informatics (197) Computer-Aided Diagnosis (258) User Needs and Competencies (464)

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 (4) Tweetations Metrics

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  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

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