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The term 'AI in nursing' currently refers to a highly heterogeneous set of technologies, ranging from predictive analytics and natural language processing to robotic systems and immersive training environments, that differ substantially in their computational paradigms, implementation environments, and relationships to professional nursing work. Existing reviews consistently document this breadth but also reveal the absence of a classification framework for these technologies. To address this gap, this contribution introduces the ‘AI Cube’, a three-dimensional taxonomy for classifying AI applications within professional nursing practice. The taxonomy structures AI applications along three analytically independent axes: implementation space (X-axis, X1-X5), AI intensity (Y-axis, Y0-Y4), and domain of professional nursing practice (Z-axis, Z1-Z4). A complementary Governance Matrix cross-tabulates AI autonomy and clinical criticality, yielding a risk-differentiated instrument for deployment evaluation that operates independently of AI intensity. The proposed taxonomy enables systematic mapping of heterogeneous AI applications, supports comparative analysis across studies, and provides a conceptual basis for governance-oriented evaluation of AI in nursing contexts.
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Flemming et al. (2026) studied this question.
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