โก Quick Summary
A recent study explored the acceptance and readiness for artificial intelligence (AI) among health care practitioners in the United Arab Emirates (UAE). The findings revealed that trust plays a crucial role in enhancing AI acceptance, with significant implications for clinical practice and training.
๐ Key Details
- ๐ Participants: 182 health care practitioners from the UAE, including physicians, nurses, dentists, and allied health staff.
- ๐งฉ Constructs assessed: Trust, perception, perceived risk, perceived benefit, acceptance, and readiness.
- โ๏ธ Methodology: Cross-sectional online survey with confirmatory factor analysis and structural equation modeling.
- ๐ Key metrics: Standardized root-mean-square residual of 0.068, goodness-of-fit index of 0.802, comparative fit index of 0.906.
๐ Key Takeaways
- ๐ค Trust is positively associated with perception and perceived benefits of AI.
- โ ๏ธ Perceived risk negatively impacts acceptance of AI technologies.
- ๐ Acceptance strongly predicts readiness to implement AI in clinical workflows.
- ๐ Knowledge gaps among practitioners highlight the need for targeted education and training.
- ๐ Findings primarily reflect the Dubai health regulatory environment and nursing workflows.
- ๐ Implementation programs should focus on building trust and reducing perceived risks.
- ๐ก Demonstrations of AI benefits should align with clinical workflows to enhance acceptance.
๐ Background
The integration of artificial intelligence (AI) in health care has the potential to significantly improve diagnostic accuracy and decision-making. However, the successful implementation of AI technologies relies heavily on the acceptance and readiness of health care practitioners to incorporate these tools into their clinical workflows. The UAE, with its proactive national AI strategies and rapid digitization of health systems, provides a unique context for examining these factors.
๐๏ธ Study
This exploratory cross-sectional survey aimed to develop and validate a model that explains how various factorsโsuch as trust, perceptions, perceived risk, and perceived benefitโaffect health care practitioners’ acceptance of AI. The study involved 182 practitioners from diverse health care roles, primarily based in Dubai, and utilized a comprehensive online survey to gather data.
๐ Results
The analysis revealed that trust was positively associated with both perception (ฮฒ=.704; P<.001) and perceived benefit (ฮฒ=.191; P=.02), while negatively associated with perceived risk (ฮฒ=-.301; P<.001). Furthermore, acceptance was found to be a strong predictor of readiness (ฮฒ=.874; P<.001), indicating that enhancing trust and reducing perceived risks could significantly improve the readiness to implement AI in clinical settings.
๐ Impact and Implications
The findings of this study underscore the importance of trust in advancing AI acceptance among health care practitioners in the UAE. By prioritizing the development of institutional and technical trust, alongside targeted training to address knowledge gaps, health care organizations can facilitate a smoother integration of AI technologies. This approach not only aligns with national AI strategies but also enhances the overall quality of patient care.
๐ฎ Conclusion
This study highlights the critical role of trust in fostering AI acceptance and readiness among health care practitioners. As the health care landscape continues to evolve with technological advancements, it is essential to implement programs that build trust, reduce perceived risks, and demonstrate the tangible benefits of AI. The future of AI in health care looks promising, and ongoing research and training will be vital for its successful adoption.
๐ฌ Your comments
What are your thoughts on the integration of AI in health care? How do you think trust can be built among practitioners? Let’s engage in a conversation! ๐ฌ Share your insights in the comments below or connect with us on social media:
Acceptance and Readiness for AI Among United Arab Emirates-Based Health Care Practitioners: Exploratory Cross-Sectional Survey.
Abstract
BACKGROUND: Artificial intelligence (AI) can enhance diagnostic accuracy, efficiency, and decision-making in health care, but real-world impact depends on practitioners’ acceptance and readiness to use AI in clinical workflows. The United Arab Emirates offers a policy-driven context to study these factors, given active national AI strategies and rapid health system digitization.
OBJECTIVE: This study aimed 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, the Unified Theory of Acceptance and Use of Technology, and the Theory of Trust and Acceptance of Artificial Intelligence Technology.
METHODS: We conducted a cross-sectional online survey of 182 United Arab Emirates-based health care 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 and structural equation modeling, reporting standard fit indices. The survey adhered to the Checklist for Reporting Results of Internet E-Surveys guidelines, and ethics approval and electronic consent were obtained.
RESULTS: Trust was positively associated with perception (ฮฒ=.704; P<.001) and perceived benefit (ฮฒ=.191; P=.02) and negatively associated with perceived risk (ฮฒ=-.301; P<.001). Acceptance was positively associated with trust (ฮฒ=.452; P<.001), perception (ฮฒ=.459; P<.001), and perceived benefit (ฮฒ=.168; P=.002), and negatively associated with perceived risk (ฮฒ=-.140; P=.009). Acceptance strongly predicted readiness (ฮฒ=.874; P<.001). The model fit indices are standardized root-mean-square residual of 0.068, root-mean-square error of approximation of 0.0913, goodness-of-fit index of 0.802, adjusted goodness-of-fit index of 0.763, and comparative fit index of 0.906. Our knowledge assessment found notable gaps among participants, underscoring a need for education and training. Our study sample was predominantly drawn from Dubai-based health care settings (103/182, 57%) and nursing roles (71/182, 39%); therefore, these findings primarily reflect the Dubai health regulatory environment and nursing workflows and may not generalize to the broader federal health care system across all Emirates.
CONCLUSIONS: Trust is a central lever for advancing AI acceptance and implementation readiness among the study cohort of United Arab Emirates-based health care practitioners. Implementation programs should prioritize building institutional and technical trust (transparency, safety, and governance), reducing perceived risk (privacy, security, and 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.
Author: [‘Alsalloum G’, ‘Badr Y’, ‘Alzaatreh A’, ‘Shamayleh A’, ‘Kumail M’, ‘Ahmad NA’, ‘Hadjiat Y’]
Journal: JMIR AI
Citation: Alsalloum G, et al. Acceptance and Readiness for AI Among United Arab Emirates-Based Health Care Practitioners: Exploratory Cross-Sectional Survey. Acceptance and Readiness for AI Among United Arab Emirates-Based Health Care Practitioners: Exploratory Cross-Sectional Survey. 2026; 5:e80173. doi: 10.2196/80173