Artificial Intelligence Technologies Supporting Clinical Judgement in Nursing: A Scoping Review

AIM: To map and analyse how artificial intelligence technologies interact with and support clinical judgement processes in nursing across practice and educational contexts. DESIGN: Scoping review. METHODS: JBI methodology for scoping reviews. DATA SOURCES: An electronic search was conducted on 1 July 2025 across MEDLINE, CINAHL, Scopus, Web of Science, and IEEE Xplore to identify studies published since January 2015. Additional sources of grey literature included ProQuest Dissertations & Theses Global, preprint servers (medRxiv and arXiv), and websites of relevant organisations. RESULTS: Eleven studies were included. Mapped against Tanner's Clinical Judgement Model, AI applications predominantly supported early cognitive phases (noticing and interpreting) through predictive models and decision support systems, while responding and reflecting phases received minimal attention. CONCLUSION: Current AI research in nursing concentrates on computational pattern recognition, with the reflective processes central to expertise development remaining largely unexamined. Future research should examine how AI influences nurses' cognitive and interpretative processes across all phases of clinical judgement, with primary studies in nursing education representing a particularly underdeveloped priority. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The integration of artificial intelligence into nursing practice should be guided by a clear understanding of how these technologies support clinical judgement. Artificial intelligence-enabled tools must be rigorously developed, implemented, and evaluated to enhance nurses' reasoning processes while safeguarding patient safety, professional autonomy, and quality of care. IMPACT: Research on artificial intelligence in nursing rarely employs theoretical frameworks of clinical judgement, limiting understanding of how these technologies interact with cognitive processes central to professional expertise. This review provides a theoretically grounded synthesis, identifying research gaps and implementation priorities for AI development aligned with nursing clinical judgement. REPORTING METHOD: PRISMA-ScR. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct or reporting. TRIAL REGISTRATION: Protocol registered in Open Science Framework (https://osf.io; DOI: https://doi.org/10.17605/OSF.IO/UH7RA), and subsequently published in a peer-reviewed journal, DOI: https://doi.org/10.62741/ahrj.v2i4.73.