Artificial Intelligence-Based Clinical Decision Support Systems in Nursing Practice: Applications, Benefits, and Challenges. A Narrative Review
Abstract
Background: Artificial intelligence-based clinical decision support systems (AI-CDSS) are increasingly used in healthcare to support data-informed clinical decisions. In nursing practice, these systems may assist with risk assessment, medication safety, early recognition of patient deterioration, documentation, and care planning. Objective: This narrative review examines the applications, potential benefits, implementation challenges, and ethical and legal considerations associated with AI-CDSS in nursing practice. Methods: Literature published between 2017 and 2025 was identified from PubMed, CINAHL, Scopus, Web of Science, and World Health Organization publications. Sources addressing AI-CDSS applications, clinical or workflow outcomes, ethical issues, and implementation barriers in nursing-related contexts were synthesized thematically. No formal quality appraisal or meta-analysis was undertaken because this was a narrative review. Results: AI-CDSS are used in medication-related decision support, early warning and deterioration detection, intensive-care monitoring, nursing documentation, and community-based care. Their potential benefits include more timely access to clinical information, support for guideline-concordant care, improved prioritization of patients at risk, and reduced administrative workload. However, effectiveness depends on reliable and interoperable data, integration with nursing workflow, usability, staff competence, and ongoing clinical oversight. Key barriers include high implementation costs, inadequate digital infrastructure, privacy and cybersecurity risks, algorithmic bias, limited transparency, and variable AI literacy among nurses. Conclusion: AI-CDSS should be regarded as tools that support, rather than replace, nursing clinical judgment. Safe and equitable implementation requires robust digital infrastructure, nurse education, local evaluation of system performance, multidisciplinary governance, and clear safeguards for privacy, transparency, accountability, and bias mitigation.