Editorial: New perspectives on the role of sensory feedback in speech production, volume III.

Generative artificial intelligence (GAI) is increasingly being integrated into healthcare (). Large language models (LLMs) such as ChatGPT have shown potential in clinical decision support, nursing documentation, and research assistance (, ). In China, the National Health Commission has issued policy guidance to promote the deep application of AI in nursing management, clinical decision-making, and patient services (). Registered nurses, as the primary workforce in clinical settings, are central to determining whether GAI can be safely and effectively integrated into nursing practice (). However, the use of GAI also raises concerns regarding content accuracy, misinformation, data privacy, and academic ethics (, ). Although qualitative studies have explored nurses’ experiences with GAI in various contexts, most have been confined to single settings or specific populations, lacking a systematic synthesis (, ). Moreover, differences in healthcare systems and cultural contexts limit the direct applicability of findings from one country to another (). In this review, artificial intelligence (AI) is used as an umbrella term referring to computational systems that perform tasks requiring human-like intelligence. Generative artificial intelligence (GAI) is a subset of AI that can generate new content, such as text, images, audio, or other outputs, based on user prompts and training data. Large language models (LLMs) are a major type of text-generating GAI designed to process and generate human language. ChatGPT is a specific LLM-based application and represents one of the most widely discussed GAI tools in healthcare and nursing contexts. Because the included studies mainly focused on text-generating GAI or LLM-based tools, particularly ChatGPT, the findings of this review should be interpreted as primarily applying to text-based GAI systems rather than to all forms of AI. We acknowledge that AI, GAI, LLMs, and ChatGPT differ in technological scope, accessibility, functions, implementation contexts, and user experiences. The following questions guided this review: (1) What are registered nurses’ core experiences of using GAI in clinical and research settings? (2) What challenges do they encounter? (3) What support needs do they express for the safe and effective use of GAI? This qualitative evidence synthesis was reported according to the PRISMA 2020 statement and the ENTREQ guideline for enhancing transparency in reporting qualitative evidence syntheses (, ). The review was registered in PROSPERO at the protocol stage (CRD420261365306). No major deviations from the registered protocol occurred in terms of the review question, eligibility criteria, databases searched, or synthesis approach. During revision, the JBI ConQual approach was added to assess the confidence of the synthesized findings and to improve the transparency and interpretability of the evidence synthesis. A systematic literature search was conducted in PubMed, CINAHL, Embase, PsycINFO, Scopus, Web of Science, the Cochrane Library, CNKI, Wanfang, VIP, and the China Biomedical Literature Database from database inception to April 25, 2026. Searches were completed between April 16 and April 25, 2026, and database-specific search dates are provided in .