aceA 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-r…
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.
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.
Evidence
- Peer-reviewedNursing Outlook2026-10-07
How should this claim be treated?
Truvace Impact Record TRV-2026-1760, v1: “A tripartite framework for nursing artificial intelligence ethics: A scoping review.” Truvace, 2026-10-18. /record/TRV-2026-1760 (accessed at citation time). sha256 071a1c5c4f01dc99…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1760 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.