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

Datos Bibliográficos

ID22073017
AutoresPeter 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, autor de correspondencia), 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)
Año2023
Volumen11
Páginas1301563-1301563
Fecha de publicación2023-11-28
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1301563
PMID38089040
OpenAlexW4389089667
IdiomaEN
Citas recibidas4
Referencias citadas31

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