Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines

BACKGROUND: In 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. METHODS: Workshop 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. CONCLUSION: Nurse 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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Publication Details

Journal
Nursing Outlook
Published
2026-04-11
DOI
https://doi.org/10.1016/j.outlook.2026.102770
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines

Oonjee Oh, Hannah Cho, Sang Bin You, George Demiris et al.
Nursing Outlook
Artificial Intelligence in Healthcare and Education
article

Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines

Oonjee Oh, Hannah Cho, Sang Bin You, George Demiris, Connie M. Ulrich, Antonia M. Villarruel
article en

Abstract

BACKGROUND: In 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. METHODS: Workshop 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. CONCLUSION: Nurse 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.

Nursing OutlookVol. 74(3)
University of Pennsylvania (US)
Hillman Foundation, Rita and Alex Hillman Foundation, National Institutes of Health, National Institute on Aging
Openalex Percentile: Top 31%
Artificial Intelligence in Healthcare and Education
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