ArticleNursing outlook
Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines.
Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Abstract
backgroundIn the era of artificial intelligence (AI), nursing science has the potential to enable transformative change in healthcare driven by the nursing clinical focus, its deep commitment to improving patient and family outcomes, its legacy of compassion, and tradition of creative innovation. PURPOSE: Inspired by discussions from a 2-day interdisciplinary workshop with experts in nursing, medicine, informatics and data science, bioethics, and the healthcare industry, this white paper provides guidelines for integrating AI in nursing science.
methodsWorkshop proceedings were transcribed and analyzed. We examined the clinical, ethical, and social implications of AI integration in nursing science, considering AI both as a topic of study and as a methodological tool, while addressing its opportunities and concerns. DISCUSSION: Drawing on these insights, we recommend several future directions of nursing science. Key priorities include integrating AI literacy as core components of graduate nursing education, expanding nursing scientists' participation in interdisciplinary AI working groups, applying rigorous implementation science frameworks to optimize AI deployment, and advocating for the interests of patients and families within this evolving landscape. We also discuss the importance of sustained collaboration with industry partners.
conclusionNurse scientists contribute expertise in the clinical and relational aspects of care, while AI designers and engineers bring essential technical insight. Such reciprocal partnerships will be essential to embed nursing science into AI development and to support the iterative innovation cycle that requires ongoing validation and trust.
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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 OpenQuestion graph.