Nursing: Ethical Dimensions and Patient Safety Impacts of Artificial Intelligence-Assisted Clinical Decision-Making in Critical Care Nursing: A Qualitative Systematic Review
DOI:
https://doi.org/10.67167/vertex.740Keywords:
Artificial intelligence, Bioethics, Clinical decision support systems, Critical care nursing, Digital workflow friction, Patient safety, Qualitative systematic reviewAbstract
Artificial intelligence (AI) and machine learning clinical decision-support systems (CDSS) are changing the workflow of critical care nurses, delivering automated physiological monitoring, predictive deterioration signals and track-and-trigger warnings. By providing real-time predictive insights, these computational tools improve patient outcomes. However, it raises bioethical issues and transforms bedside clinical assessments. While a thorough quantitative assessment has been conducted on the accuracy of artificial intelligence, the qualitative navigation of care nurses is essential to address issues of ethics, professional autonomy, and patient safety risks. Critical care nurses' experiences with artificial intelligence-based clinical decision-making are studied in this empirical qualitative study. From inception until 2026, researchers used PubMed, CINAHL, Scopus, and ScienceDirect as reference sources. Included in the following criteria: peer-reviewed empirical qualitative studies; registered nurse research in critical care, ICUs, and NICUs using artificial intelligence/CDSS technology; emphasis on patient ethics and safety; and access to complete English texts. The CASP Qualitative Checklist evaluates ten research that meet all inclusion criteria. The synthesis yields three key concepts: automation has a bias over clinical intuition; accountability for AI-driven clinical errors; and digital workflow friction that affects timely patient interventions. Uncalibrated alerts, non-transparent assessment models, and ambiguity of responsibility lead to cognitive burden, desensitization of warnings, and escalation delays. The safety of sociotechnical integration includes institutional nursing discretion, calibrated alert thresholds, and transparent override processes.
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