NURSES' PERCEPTIONS, KNOWLEDGE AND EXPERIENCE REGARDING ARTIFICIAL INTELLIGENCE IN A TERTIARY HEALTHCARE SETTING: A QUANTITATIVE CROSS-SECTIONAL STUDY

Abstract

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare systems to improve efficiency, support clinical decision-making, and address workforce shortages. Nurses are central to patient care delivery and play a key role in the successful adoption of AI technologies. However, limited evidence exists regarding nurses’ knowledge and perceptions of AI in public-sector hospitals in Pakistan. This study aimed to assess nurses’ knowledge, perceptions, and attitudes toward artificial intelligence at Mardan Medical Complex, Mardan. Methods: A cross-sectional descriptive study was conducted among nurses working at Mardan Medical Complex, Mardan. A total of 172 nurses were recruited using a convenience sampling technique. Data were collected using a structured, self-administered questionnaire used in previous study covering demographic characteristics, knowledge of artificial intelligence, and perceptions regarding its use in nursing practice. Data were analyzed using SPSS. Descriptive statistics (frequencies and percentages) were used to summarize the data. Spearman’s rank correlation and Cramer’s V tests were applied to examine associations between demographic variables, AI knowledge, and perceptions, with a significance level set at p < 0.05. Results: The study found that the majority of nurses had limited knowledge of artificial intelligence, with 74.8% (n = 129) reporting low or basic understanding of AI, While only 25.2% (n = 43) reported good or advanced knowledge. Despite limited knowledge, most nurses demonstrated a positive attitude toward AI, with 65.7% (n = 113) perceiving AI as an opportunity in nursing practice, whereas 13.7% (n = 24) viewed it as a threat and 20.6% (n = 35) were unsure. Nurses most commonly identified AI applications in patient monitoring (55.7%), route planning and workflow management (47.7%), and nursing documentation (43.6%). Spearman’s rank correlation showed a significant positive relationship between education level and AI knowledge (ρ = 0.312, p = 0.001), while no significant correlation was found between age and AI knowledge (ρ = 0.087, p = 0.251). Cramer’s V analysis revealed significant associations between gender and AI knowledge (V = 0.239, p = 0.002), education level and AI knowledge (V = 0.329, p < 0.001), and AI knowledge and perception of AI (V = 0.259, p = 0.003). Conclusion: Although nurses at Mardan Medical Complex have limited knowledge of artificial intelligence, the majority perceive AI positively and recognize its potential to support nursing practice. The gap between low AI literacy and high acceptance highlights the need for targeted education, in-service training programs, and institutional policies that actively involve nurses in AI-related initiatives. Strengthening nurses’ AI knowledge may facilitate the ethical, effective, and patient-centered integration of artificial intelligence into nursing services