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TRUVACE RECORD VERSION
record: TRV-2026-1760
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T01:09:00.824572Z
status: published
lens: p_space
sector: labor
headline: A tripartite framework for nursing artificial intelligence ethics: A scoping review
dek: 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-r…
gain_title: (none)
problem_title: Nurses and nursing students report ethical risks from AI, including deskilling through dependence, opaque algorithms, and an unresolved responsibility gap over liability for AI-driven errors.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Nurses and nursing students report ethical risks from AI, including deskilling through dependence, opaque algorithms, and an unresolved responsibility gap over liability for AI-driven errors.
problem_evidence: a pervasive responsibility gap regarding professional liability for AI-driven errors
quick_read: Researchers reviewed peer-reviewed literature from 2014 through 2025 on how nurses and nursing students view the ethical design and use of AI. Of 1,259 records found, 50 articles were analyzed and their themes were matched to American Nurses Association and International Council of Nurses ethics codes.

The review matters because AI is entering nursing practice, education, and research faster than profession-specific ethical guidance. The authors call for dedicated frameworks, shared accountability, and training. As a synthesis of earlier studies, it does not measure how any AI tool affects patient or workforce outcomes.
limitation: The article is a scoping review of existing literature about perspectives on AI ethics, so it synthesizes themes rather than testing outcomes, and the authors say dedicated frameworks and accountability models are still needed.
tag: Evidence-backed problem
key_points: The review screened 1,259 records and included 50 articles published between 2014 and 2025. | Nine themes were grouped into user-focused, technology-focused, and contextual domains. | User-focused themes were autonomy, erosion of knowledge and skills, and human contact and empathy. | Technology-focused themes were privacy and security, performance and reliability, and transparency and explainability. | The authors say AI should be a supportive adjunct rather than a replacement for nursing judgment.
rundown: The framework's contextual domain covers justice and equity, liability and accountability, and education and training. These sit alongside the user-focused and technology-focused domains and reflect the wider setting in which nurses use AI.

The authors identify friction between algorithmic outputs and clinical intuition as a critical tension. They also point to the black box nature of opaque algorithms as a concern for professional practice.

The review's conclusion is that the profession should move from ethical anxiety to ethical stewardship. It recommends dedicated nursing-AI ethical frameworks, shared accountability models, and robust educational initiatives.
sources:
- peer_reviewed | Nursing Outlook | https://doi.org/10.1016/j.outlook.2026.102908 | 2026-10-07
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