Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.

BACKGROUND

Artificial intelligence (AI) has demonstrated transformative potential in the health care field; yet, its clinical adoption faces challenges such as inaccuracy, bias, and data privacy concerns. As the primary operators of AI systems, physicians and nurses play a pivotal role in integrating AI into clinical workflows. Their acceptance and use of AI are essential for bridging the gap between technological innovation and practical implementation. Exploring Chinese medical staff's attitudes and identifying key factors influencing AI adoption are fundamental to developing targeted strategies to facilitate the effective application of AI in clinical settings.

OBJECTIVE

This study aims to investigate attitudes and perceptions regarding medical AI among physicians and nurses in China and identify the factors influencing its adoption.

METHODS

A nationwide cross-sectional survey was conducted online from December 12 to 26, 2024. Participants were recruited from the Chinese Medical Association and the Chinese Nursing Association. The structured questionnaire assessed demographic characteristics, knowledge and attitudes toward medical AI, experiences and insights regarding using medical AI, and perceptions and factors influencing the adoption of AI based on the unified theory of acceptance and use of technology (UTAUT) model. Multiple linear regression and Karlson-Holm-Breen mediation analysis were used to identify influencing factors. Sample weighting by regional distribution was applied for sensitivity analysis.

RESULTS

The survey included 991 physicians and 1714 nurses. Among the respondents, 92.4% (916/991) of the physicians and 84.19% (1443/1714) of the nurses reported awareness of medical AI applications, 22.8% (226/991) of the physicians and 17% (291/1714) of the nurses had used AI, and 82.6% (819/991) of the physicians and 80.22% (1375/1714) of the nurses held optimistic views about AI's prospects. After adjusting for covariates, performance expectancy (physicians: B=0.144, 95% CI 0.092-0.197; nurses: B=0.292, 95% CI 0.245-0.338), effort expectancy (physicians: B=0.681, 95% CI 0.562-0.800; nurses: B=0.440, 95% CI 0.342-0.538), social influence (physicians: B=0.264, 95% CI 0.187-0.341; nurses: B=0.098, 95% CI 0.045-0.152), and facilitating conditions (physicians: B=0.098, 95% CI 0.030-0.165; nurses: B=0.158, 95% CI 0.105-0.212) had significant positive impacts on willingness to use AI. Perceived risk showed no significant effect on physicians' intention to use AI (B=0.012, 95% CI -0.022 to 0.045) but negatively impacted nurses' intention to use AI (B=-0.041, 95% CI -0.066 to -0.015). Performance expectancy and effort expectancy partially mediated the relationship between facilitating conditions and intention to use. Age, educational level, hospital level, work experience, and personal views also significantly influenced willingness. Weighted and unweighted analyses yielded consistent results, confirming the robustness of the findings.

CONCLUSIONS

Substantial disparities exist between high willingness to adopt medical AI and its low actual use among Chinese medical staff. System optimization focusing on utility enhancement, workflow integration, and risk mitigation for medical staff, along with support for infrastructure and training, could accelerate AI adoption in clinical practice.