Modeling the influence of attitudes, trust, and beliefs on endoscopists’ acceptance of artificial intelligence applications in medical practice
Bibliographic Data
| ID | 22073017 |
|---|---|
| Authors | Peter J Schulz (0000-0003-4281-489X, Nanyang Technological University), May O Lwin (0000-0003-1832-8242, Nanyang Technological University), Kalya M Kee (0000-0001-9454-1534, Nanyang Technological University, corresponding author), Wilson Wen Bin Goh (0000-0003-3863-7501, Nanyang Technological University), Wilson W B Goh, Thomas Y T Lam, Thomas Y Lam (0000-0002-4306-4990, Chinese University of Hong Kong), Joseph J �Y Sung (0000-0003-3125-5199, Nanyang Technological University) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1301563-1301563 |
| Publication date | 2023-11-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1301563 |
| PMID | 38089040 |
| OpenAlex | W4389089667 |
| Language | EN |
| Citations received | 4 |
| References cited | 31 |
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: = 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
Clinical Practice · Family medicine · Medical education · Software deployment · Technology Acceptance Model · Usability · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Computer Science · Electronic Health Records Systems · Medicine · Psychology
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| Unique citing works | 4 |
|---|---|
| Citations per year | 4 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |