Dentists’ acceptance of artificial intelligence-assisted decision support for oral potentially malignant disorder detection: a structural equation modeling study
Abstract
Artificial intelligence (AI) may support early detection and clinical decision-making of oral potentially malignant disorders (OPMDs), but clinical implementation depends on dentists’ acceptance of AI-assisted clinical decision support. This study aimed to identify structural associations of AI acceptance among dentists using an extended Technology Acceptance Model (TAM). A cross-sectional survey was conducted among 500 dentists. Construct validity and reliability were assessed using confirmatory factor analysis. Hypothesized associations were evaluated using structural equation modeling with bootstrap mediation analysis. The extended TAM showed generally satisfactory measurement properties and good overall model fit. Attitude toward AI showed the strongest association with behavioral intention (β = 0.550), followed by AI-assisted clinical decision reliance (β = 0.220), perceived usefulness (β = 0.180), trust (β = 0.140), and perceived ease of use (β = 0.120). Significant indirect effects operated through attitude toward AI. The model explained 67.5% of the variance in behavioral intention. Dentists reported favorable behavioral intention toward AI-assisted decision support for OPMD detection. Attitude toward AI showed the strongest association with behavioral intention. The findings are consistent with the extended TAM. Successful implementation will require explainable systems, clinician education, trust-building, and effective workflow integration. Longitudinal studies are needed to determine whether these associations translate into actual clinical adoption. Understanding factors associated with dentists’ acceptance of AI may support the responsible integration of AI-assisted decision support into oral cancer screening pathways.
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BibTeX
@article{ghalwash2026dentists,
title = {{Dentists’ acceptance of artificial intelligence-assisted decision support for oral potentially malignant disorder detection: a structural equation modeling study}},
author = {Dalia Ghalwash},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73787-y},
url = {https://www.nature.com/articles/s41598-026-73787-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dentists’ acceptance of artificial intelligence-assisted decision support for oral potentially malignant disorder detection: a structural equation modeling study
AU - Dalia Ghalwash
PY - 2026
DA - 2026/10/30
JO - Scientific Reports
DO - 10.1038/s41598-026-73787-y
UR - https://www.nature.com/articles/s41598-026-73787-y
AB - Artificial intelligence (AI) may support early detection and clinical decision-making of oral potentially malignant disorders (OPMDs), but clinical implementation depends on dentists’ acceptance of AI-assisted clinical decision support. This study aimed to identify structural associations of AI acceptance among dentists using an extended Technology Acceptance Model (TAM). A cross-sectional survey was conducted among 500 dentists. Construct validity and reliability were assessed using confirmatory factor analysis. Hypothesized associations were evaluated using structural equation modeling with bootstrap mediation analysis. The extended TAM showed generally satisfactory measurement properties and good overall model fit. Attitude toward AI showed the strongest association with behavioral intention (β = 0.550), followed by AI-assisted clinical decision reliance (β = 0.220), perceived usefulness (β = 0.180), trust (β = 0.140), and perceived ease of use (β = 0.120). Significant indirect effects operated through attitude toward AI. The model explained 67.5% of the variance in behavioral intention. Dentists reported favorable behavioral intention toward AI-assisted decision support for OPMD detection. Attitude toward AI showed the strongest association with behavioral intention. The findings are consistent with the extended TAM. Successful implementation will require explainable systems, clinician education, trust-building, and effective workflow integration. Longitudinal studies are needed to determine whether these associations translate into actual clinical adoption. Understanding factors associated with dentists’ acceptance of AI may support the responsible integration of AI-assisted decision support into oral cancer screening pathways.
SN - 2045-2322
ER -