Research trends in generative artificial intelligence in nursing: a scoping review

Purpose: Generative artificial intelligence (AI) has yet to be comprehensively analyzed in the nursing literature. This study aimed to identify research trends in generative AI within the nursing field through a scoping review and propose strategies for its effective utilization in nursing. Methods: A scoping review was conducted following Arksey and O’Malley’s six-stage framework. The inclusion criteria included: (1) studies conducted in nursing; (2) research related to generative AI; and (3) original research articles, theses, communications, editorials, letters, or commentaries published in academic journals. Database used PubMed, Embase, CENTRAL, CINAHL, KMbase, KoreaMed, KISS, ScienceON, RISS, DBpia, and 27 nursing-specific journals. Results: In total, 403 studies were initially identified, and 58 were included in the final analysis. In the care domain, strengths included rapid information retrieval and improved nurse-patient communication, while limitations included the irreplaceable human element and low reliability. The administration domain had no relevant studies. In the research domain, generative AI exhibited strengths such as enhanced efficiency in the paper writing process and improved dissemination speed, but its weaknesses included lack of ethical and legal accountability and a risk of inaccurate or biased information. In the education domain, generative AI was effective in saving time in educational design and implementation, as well as supporting content creation, but challenges included algorithmic bias and risks of plagiarism. Conclusion: This study identified potential benefits and limitations of generative AI across nursing domains. For effective application, it is essential to develop comprehensive guidelines and policies, provide user education and support, and create opportunities for nurses, educators, and students to learn about strengths and risks of generative AI.