AI in the NICU: Considerations for Nursing Practice and Advocacy in Quality Care Outcomes for Patients and Families.

BACKGROUND: Neonatal nurses and APRNs may not recognize when AI drives an alert, recommendation, or summary unless training and policy make it explicit. Nurses protect infants and practice when they treat AI output as clinical decision support, not clinical truth. Limited transparency about "black box" performance requires nurses to verify information, increases professional risk, and blurs practice boundaries when AI-driven recommendations conflict with clinical judgment. PURPOSE: Provide practical guidance to identify where AI appears in workflow, apply a "nurse as verifier" stance, and use clear documentation and escalation pathways when AI affects care. METHODS: We describe common AI encounters in the NICU summarization tools, EHR-based clinical decision support, and embedded AI in monitors and sensors. We outline actions to verify outputs, reduce automation bias, preserve an audit trail when AI influences escalation or de-escalation, and report AI-related errors. RESULTS: AI can strengthen situational awareness, but it does not replace nursing judgment or accountability. In a high-acuity environment with rapid physiologic change, undisclosed or poorly understood AI increases risk, especially when nurses cannot confirm data sources or limitations underlying clinical decision support outputs. IMPLICATIONS FOR PRACTICE: Nurses carry responsibility to determine when AI use supports safety and when it introduces risk within their scope of practice and standards of care. These responsibilities increasingly align with evolving legal and regulatory expectations related to AI-informed actions and documentation. IMPLICATIONS FOR RESEARCH: Research is needed to determine how nurses use AI in current workflow and identify risk to nursing practice and infant safety.