JMIR Publications

ABSTRACT

Background:

Background:

Assessing the current landscape of nurses' knowledge and attitudes is a critical first step in facilitating a smooth and effective transition towards AI-enhanced critical care.

Objective:

Objectives: We aimed in this study to assess the levels of and factors affecting the knowledge and general attitudes of critical care nurses towards artificial intelligence (AI).

Methods:

Methods:

A cross-sectional correlational design was used. Data were collected using the Nurses' AI Knowledge Questionnaire and the 20-item General Attitudes toward AI Scale from May to July 2025. Using multivariate linear regression analysis, the significant factors affecting CCNs’ knowledge and attitudes were identified. Correlation between variables was assessed using Pearson's correlation coefficient. P-value was set at less than .05.

Results:

Results:

The mean scores for CCNs’ knowledge and attitude towards AI were 4.93 ± 1.78 and 64.39 ± 8.26, respectively, indicating a moderate level of knowledge and a positive attitude towards AI. CCNs’ knowledge of AI was positively and significantly correlated with their attitude towards AI (r = .450, p < .001). Nurses aged 30–39 years (β = –.804, p = .017) and those aged 40 years or older (β = –1.285, p = .003) had lower knowledge scores than those aged 20–29 years. Similarly, female nurses reported significantly lower knowledge scores than their male counterparts (β = −.697, p = .007). In contrast, nurses with more than 5 years of experience had significantly higher knowledge levels (β = 1.203, p < .001). The model explained 19.4% of the variance in knowledge (Adjusted R² = .169, p < .001). Regarding attitudes, nurses aged 30–39 years (β = –4.806, p = .001) and those aged 40 years or older (β = –8.969, p < .001) reported less positive attitudes than those aged 20–29 years. Female nurses had significantly less positive attitudes than male nurses (β = –2.649, p = .015). Conversely, nurses with a master’s degree (β = 3.381, p = .002) and those with more than 5 years of experience (β = 8.084, p < .001) demonstrated more positive attitudes. The model explained 33.2% of the variance in attitude (Adjusted R² = .311, p < .001).

Conclusions:

Conclusion: CCNs in Hail City demonstrated moderate knowledge and positive attitudes toward AI, with knowledge positively correlated with attitudes. Age, sex, education, and clinical experience significantly influenced these perceptions. These findings highlight that the successful integration of AI into critical care depends on enhancing nurses’ literacy, confidence, and engagement through continuous, inclusive education, and involvement in AI development processes. Strengthening these aspects will facilitate effective AI adoption and support Saudi Arabia’s Vision 2030 for a technology-driven health care system. Clinical Trial: NA