Generative artificial intelligence in cardiovascular specialty care: a scoping review.

BACKGROUND

The current research on the application of generative artificial intelligence in has shown rapid growth, and most of the existing research focuses on general nursing scenarios. And is still in the exploratory stage in the specialty field and lacks a systematic compendium of its application in the field of cardiovascular specialty nursing.

OBJECTIVE

A review of studies related to the application of generative artificial intelligence in the field of specialized cardiovascular nursing informing relevant research and practice.

METHODS

This review was conducted according to the JBI guidelines for scoping reviews, using the PRISMA-ScR reporting tool.The databases searched included PubMed, Embase, Web of Science, APA PsycNetInfo (Ovid), the Cochrane Library, MEDLINE (Ovid), CINAHL, SinoMed, CNKI, WanFang databases, peer-reviewed studies written in English and Chinese from the time of database construction until March 29, 2025. Eligible studies were screened by title and abstract, and full-text screening was performed by two independent evaluators.

RESULTS

Nineteen studies were included in this review, and the main application tools were 9 text generation models, 3 multimodal generation models, 4 temporal prediction models, and 3 chatbots. The application scenarios mainly include clinical decision support, patient health management, and nursing education and counseling. Generative artificial intelligence has outstanding effects in reducing nursing workload and precise intervention, but there are limitations in the field of health education and counseling, such as logical disconnection, poor information quality, and lack of humanistic care.

CONCLUSIONS

Generative artificial intelligence provides technical support for the intelligent transformation of cardiovascular specialty care, especially effective in the areas of clinical decision support and patient health management. There is an urgent need to solve the existing problems to promote its in-depth application in this field, and it is suggested that future research focuses on the construction of specialized multimodal models.