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

Formative Evaluation of Digital Health Interventions (2414) Adoption and Change Management of eHealth Systems (712) Artificial Intelligence (1371) Decision Support for Health Professionals (1185)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33368, first published .
The Drivers of Acceptance of Artificial Intelligence–Powered Care Pathways Among Medical Professionals: Web-Based Survey Study

The Drivers of Acceptance of Artificial Intelligence–Powered Care Pathways Among Medical Professionals: Web-Based Survey Study

The Drivers of Acceptance of Artificial Intelligence–Powered Care Pathways Among Medical Professionals: Web-Based Survey Study

Authors of this article:

Lisa Cornelissen1 Author Orcid Image ;   Claudia Egher1, 2 Author Orcid Image ;   Vincent van Beek3, 4 Author Orcid Image ;   Latoya Williamson3 Author Orcid Image ;   Daniel Hommes3, 4 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Barwise A, Curtis S, Diedrich D, Pickering B. Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives. Journal of the American Medical Informatics Association 2024;31(3):611 View
  2. Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J, Hort S, Vrijhoef H. Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors 2024;11:e47031 View
  3. Kijpaisalratana N, Saoraya J, Nhuboonkaew P, Vongkulbhisan K, Musikatavorn K. Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial. Internal and Emergency Medicine 2024;19(5):1415 View
  4. Alkhatieb M, Subke A. Artificial Intelligence in Healthcare: A Study of Physician Attitudes and Perceptions in Jeddah, Saudi Arabia. Cureus 2024 View
  5. Dingel J, Kleine A, Cecil J, Sigl A, Lermer E, Gaube S. Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology. Journal of Medical Internet Research 2024;26:e57224 View
  6. Shonhe L. Conceptual framework to explore artificial intelligence technology (AIT) readiness and adoption intention in records and information management (RIM) practices: a proposal. Records Management Journal 2024 View
  7. El Tantawi M, Ammar N, Mariño R, Uribe S, Manton D, Hugo F, Clément C, Sim C, Maret D, Kopycka-Kedzierawski D, Mbende E, Kruger E, Lan R, Doghri L, Castelaz M, Alam M, Ibiyemi O, Naidoo S, Schwarz E, Priya H, Braga M, Giraudeau N, Foláyan M. Developing the teledentistry acceptance survey for dentists – TAS-D: a Delphi study. BMC Oral Health 2024;24(1) View
  8. Khanfar A, Kiani Mavi R, Iranmanesh M, Gengatharen D. Determinants of artificial intelligence adoption: research themes and future directions. Information Technology and Management 2024 View

Books/Policy Documents

  1. Parsonage G, Horton M, Read J. Adaptive Instructional Systems. View
  2. . Transformative Impacts of AI in Management. View