<![CDATA[Artificial intelligence–assisted clinical decision support systems are increasingly integrated into smart healthcare environments, offering new opportunities to enhance patient safety and care quality while transforming nursing practice. Persistent challenges such as medical errors, delayed clinical responses, and variability in care highlight the need for effective decision support tools. This study aims to examine the impact of artificial intelligence-assisted systems on patient safety, care quality, and nursing decision-making processes. A mixed-methods design was employed, combining quantitative analysis of clinical indicators with qualitative insights from nurses across multiple hospital units. Data were collected from 120 nurses and corresponding patient records before and after system implementation, supported by surveys and interviews. Findings reveal significant reductions in medication errors and adverse events, alongside improvements in response time, care quality, and nurse decision confidence. Inferential analysis confirms that system usability and training significantly influence outcomes, while experience level moderates system effectiveness. The study concludes that artificial intelligence–assisted decision support enhances clinical performance by complementing nursing expertise and enabling data-driven decision-making. Effective integration depends on user readiness, organizational support, and alignment with clinical workflows, highlighting the need for human-centered implementation strategies in smart healthcare systems.]]>
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