A tripartite framework for nursing artificial intelligence ethics: A scoping review

Background As artificial intelligence (AI) rapidly integrates into nursing practice, education, and research, it presents a profound ethical paradox: significant opportunities for operational efficiency alongside deep-seated challenges to the profession's core values. Purpose This scoping review synthesizes existing literature to explore the perspectives of nurses and nursing students regarding the ethical design, implementation, and use of AI technologies. Methods A scoping review was conducted analyzing peer-reviewed articles published from 2014 through 2025. Following a systematic search that identified 1,259 potential records, 50 articles met inclusion criteria for final analysis. Data were extracted to identify core ethical themes, which were subsequently aligned with the professional standards of the American Nurses Association (ANA) and International Council of Nurses (ICN) codes of ethics. Discussion Findings were synthesized into a tripartite framework comprising nine themes across three domains: (1) User-Focused (Autonomy, Erosion of Knowledge/Skills, and Human Contact/Empathy); (2) Technology-Focused (Privacy/Security, Performance/Reliability, and Transparency/Explainability); and (3) Contextual (Justice/Equity, Liability/Accountability, and Education/Training). Critical tensions identified include the friction between algorithmic outputs and clinical intuition, the risk of deskilling through AI dependency, the black box nature of opaque algorithms, and a pervasive responsibility gap regarding professional liability for AI-driven errors. Conclusion For AI to be successfully integrated, it must function as a supportive adjunct rather than a replacement for nursing judgment and the irreplaceable human-to-human connection. Transitioning the profession from ethical anxiety to ethical stewardship requires the urgent development of dedicated nursing-AI ethical frameworks, shared accountability models, and robust educational initiatives. Such measures are essential to ensure that emerging technologies reinforce, rather than erode, the foundational values of the nursing profession.

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Publication Details

Journal
Nursing Outlook
Published
2026-10-07
DOI
https://doi.org/10.1016/j.outlook.2026.102908
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

A tripartite framework for nursing artificial intelligence ethics: A scoping review

Aissa Feldmann, Suzanne Fink, Jenna L. Marquard, Chanhee Kim et al.
Nursing Outlook
Artificial Intelligence in Healthcare and Education
article

A tripartite framework for nursing artificial intelligence ethics: A scoping review

Aissa Feldmann, Suzanne Fink, Jenna L. Marquard, Chanhee Kim, Malin Britt Lalich, Connie White Delaney, Winnie Yip
article en

Abstract

Background As artificial intelligence (AI) rapidly integrates into nursing practice, education, and research, it presents a profound ethical paradox: significant opportunities for operational efficiency alongside deep-seated challenges to the profession's core values. Purpose This scoping review synthesizes existing literature to explore the perspectives of nurses and nursing students regarding the ethical design, implementation, and use of AI technologies. Methods A scoping review was conducted analyzing peer-reviewed articles published from 2014 through 2025. Following a systematic search that identified 1,259 potential records, 50 articles met inclusion criteria for final analysis. Data were extracted to identify core ethical themes, which were subsequently aligned with the professional standards of the American Nurses Association (ANA) and International Council of Nurses (ICN) codes of ethics. Discussion Findings were synthesized into a tripartite framework comprising nine themes across three domains: (1) User-Focused (Autonomy, Erosion of Knowledge/Skills, and Human Contact/Empathy); (2) Technology-Focused (Privacy/Security, Performance/Reliability, and Transparency/Explainability); and (3) Contextual (Justice/Equity, Liability/Accountability, and Education/Training). Critical tensions identified include the friction between algorithmic outputs and clinical intuition, the risk of deskilling through AI dependency, the black box nature of opaque algorithms, and a pervasive responsibility gap regarding professional liability for AI-driven errors. Conclusion For AI to be successfully integrated, it must function as a supportive adjunct rather than a replacement for nursing judgment and the irreplaceable human-to-human connection. Transitioning the profession from ethical anxiety to ethical stewardship requires the urgent development of dedicated nursing-AI ethical frameworks, shared accountability models, and robust educational initiatives. Such measures are essential to ensure that emerging technologies reinforce, rather than erode, the foundational values of the nursing profession.

Nursing OutlookVol. 74(6)
University of Minnesota (US)
Openalex Percentile: Top 19%
Artificial Intelligence in Healthcare and Education
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