Nurses' responses to patients' use of artificial intelligence for health information: a scoping review

Artificial intelligence tools — large language model chatbots, symptom checkers, AI-based interpretation of wearable-device data, and AI-generated or AI-translated medical content — are increasingly used by patients and families to obtain health information, which they then bring into clinical encounters. This shifts part of nurses' work from providing health information toward identifying, appraising, and calibrating patient-acquired information, including correcting misinformation, addressing questions rooted in AI-generated advice, and negotiating situations where AI-derived information conflicts with professional care recommendations. Two parallel strands exist in the nursing literature: (a) internet-era research on "internet-informed patients" and nurses' responses (L___), and (b) research on nurse–clinical-AI interaction (L___). The intersection — patients' use of AI for health information and nurses' responses — has not been systematically mapped: concept boundaries are unclear, evidence is scattered, and much of it is descriptive or anecdotal. This scoping review aims to map the extent, range, and nature of evidence at this intersection, clarify concepts, describe how nurse responses are studied and measured, and identify evidence gaps to inform practice guidance, health-education strategies, and future primary research. AI-specific evidence is at an early stage; a limited number of eligible Tier 1 sources is anticipated, and a nascent-evidence finding will be reported candidly rather than compensated for by post hoc broadening of eligibility. Citation note: every factual claim in the background must carry an L number; the PMIDs listed in Section 3 are candidate sources, but full citation fields must be completed from the retrieved full texts.