Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice

Front Public Health. 2023 Nov 28:11:1301563. doi: 10.3389/fpubh.2023.1301563. eCollection 2023.

Abstract

Introduction: The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice.

Methods: We utilized online surveys to gather data from clinicians in the field of gastroenterology.

Results: A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians.

Discussion: The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.

Keywords: acceptance; artificial intelligence; attitudes; gastroenterology; trust.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Artificial Intelligence*
  • Educational Status
  • Endoscopy
  • Female
  • Gastroenterology
  • Humans
  • Male
  • Policy
  • Technology
  • Trust*

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-GV-2021-009).