ArticleBMC nursing2025
Generative artificial intelligence in cardiovascular specialty care: a scoping review.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From assistance to autonomy: AI agent systems in cardiovascular medicine-a review of paradigms, architectures, and clinical translation.Frontiers in cardiovascular medicine · 2026Pooled it
- Outcomes, Evaluation Metrics, and Measurement Tools for Large Language Model Applications Research in Nursing: A Scoping Review.International nursing review · 2026Article
- Application of Telemedicine and Artificial Intelligence in Outpatient Cardiology Care: TeleAI-CVD Study (Design).Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveA review of studies related to the application of generative artificial intelligence in the field of specialized cardiovascular nursing informing relevant research and practice.
methodsThis 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.
resultsNineteen 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.
conclusionsGenerative 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.