JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) can enhance diagnostic accuracy, efficiency, and decision-making in healthcare, but real-world impact depends on practitioners’ acceptance and readiness to use AI in clinical workflows. The United Arab Emirates (UAE) offers a policy-driven context to study these factors, given active national AI strategies and rapid health-system digitization.

Objective:

To develop and validate a model explaining how trust, perceptions, perceived risk, and perceived benefit shape practitioners’ acceptance of AI and, in turn, their readiness to implement AI in clinical practice. The model integrates the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Theory of Trust and Acceptance of Artificial Intelligence Technology (TrAAIT).

Methods:

We conducted a cross-sectional online survey of 182 UAE-based healthcare practitioners (physicians, nurses, dentists, and allied health staff). Constructs included trust, perception, perceived risk, perceived benefit, acceptance, and readiness. Knowledge of AI was also assessed using true or false statements. We performed confirmatory factor analysis (CFA) and structural equation modeling (SEM), reporting standard fit indices, the survey adhered to CHERRIES, and ethics approval and electronic consent were obtained.

Results:

Trust positively influenced perception (β≈0.704, P<.001) and perceived benefit (β≈0.191, P=.020) and negatively influenced perceived risk (β≈−0.301, P<.001). Acceptance was positively associated with trust (β≈0.452, P<.001), perception (β≈0.459, P<.001), and perceived benefit (β≈0.168, P=.0015), and negatively associated with perceived risk (β≈−0.140, P=.009). Acceptance strongly predicted readiness (β≈0.874, P<.001). Overall model fit was acceptable (SRMR=0.068, RMSEA=0.0913, GFI=0.802, AGFI=0.763, CFI=0.906). Our knowledge assessment found notable gaps among participants, underscoring a need for education and training.

Conclusions:

Trust is a central lever for advancing AI acceptance and implementation readiness among the study cohort of UAE-based healthcare practitioners. Implementation programs should prioritize building institutional and technical trust (transparency, safety, and governance), reducing perceived risk (privacy, security, reliability), and amplifying perceived benefits through hands-on demonstrations and workflow-aligned use cases. Targeted training to close knowledge gaps should accompany policy and organizational measures aligned with national AI strategies to accelerate responsible, clinician-in-the-loop adoption.