Background: Since the release of ChatGPT in November 2022, this emerging technology has garnered a lot of attention in various fields, and nursing is no exception. However, to date, no study has comprehensively summarized the status and opinions of using ChatGPT across different nursing fields.
Objective: We aim to synthesize the status and opinions of using ChatGPT according to different nursing fields, as well as assess ChatGPT's strengths, weaknesses, and the potential impacts it may cause.
Methods: This scoping review was conducted following the framework of Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive literature research was conducted in 4 web-based databases (PubMed, Embase, Web of Science, and CINHAL) to identify studies reporting the opinions of using ChatGPT in nursing fields from 2022 to September 3, 2023. The references of the included studies were screened manually to further identify relevant studies. Two authors conducted studies screening, eligibility assessments, and data extraction independently.
Results: A total of 30 studies were included. The United States (7 studies), Canada (5 studies), and China (4 studies) were countries with the most publications. In terms of fields of concern, studies mainly focused on "ChatGPT and nursing education" (20 studies), "ChatGPT and nursing practice" (10 studies), and "ChatGPT and nursing research, writing, and examination" (6 studies). Six studies addressed the use of ChatGPT in multiple nursing fields.
Conclusions: As an emerging artificial intelligence technology, ChatGPT has great potential to revolutionize nursing education, nursing practice, and nursing research. However, researchers, institutions, and administrations still need to critically examine its accuracy, safety, and privacy, as well as academic misconduct and potential ethical issues that it may lead to before applying ChatGPT to practice.
Keywords: ChatGPT; artificial intelligence; generative AI; large language model; nursing; nursing education; scoping review.
© You Zhou, Si-jia Li, Xing-Yi Tang, Yi-Chen He, Hao-Ming Ma, Ao-Qi Wang, Run-Yuan Pei, Mei-Hua Piao. Originally published in JMIR Medical Education (https://mededu.jmir.org).