Current landscape and research gaps in artificial intelligence for ophthalmic nursing: a scoping review
Abstract
INTRODUCTION: Artificial intelligence (AI) is advancing rapidly across clinical medicine, yet its application within ophthalmic nursing, spanning emergency triage, perioperative care, day surgery, and patient education, remains poorly characterized. The growing burden of eye disease and the demand for community-based ophthalmic screening create an urgent context for understanding where AI can augment nursing capacity in this field and what barriers may be limiting evidence generation. This review aimed to systematically map published evidence on AI applications in ophthalmic nursing practice and education, characterize the technologies deployed and outcomes reported, and identify recurring research gaps and barriers through interpretive synthesis of the mapped literature.
METHODS: Four electronic databases (Web of Science Core Collections, PubMed, Scopus, and CINAHL) were searched from inception to June 12, 2026. Peer-reviewed English-language articles simultaneously addressing AI or machine learning, ophthalmology or eye conditions, and nurse engagement were eligible. Records were independently screened by two reviewers and data were extracted using a standardized charting form.
RESULTS: From 254 records retrieved, 7 studies met the inclusion criteria (2023-2026). Applications spanned emergency triage and primary diagnosis, surgical nursing assistance (including one pre-clinical prototype without direct nurse involvement), day ward workflow management, and nursing…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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