Artificial intelligence is embedded in clinical nursing workflows across the United States. Predictive deterioration scores, sepsis screening algorithms, clinical decision support alerts, ambient documentation tools, and artificial intelligence-driven staffing models now generate recommendations that nurses encounter every shift. Yet a significant gap persists between the pace of artificial intelligence deployment and the preparation nurses receive to critically evaluate these outputs. This presentation introduces a conceptual clinical-thinking framework designed to address this gap and help nurses evaluate artificial intelligence-generated clinical recommendations before acting, identify domains of patient information that fall outside algorithmic analysis, and document artificial intelligence clinical comparisons to support professional accountability and system improvement. Attendees will leave with portable, ready-to-use tools for immediate integration into bedside practice, nursing education, and informatics governance.