Wynn, M (2025) The ethics of non-explainable artificial intelligence: an overview for clinical nurses. British Journal of Nursing, 34 (5). pp. 294-297. ISSN 0966-0461

Text The ethics of non-explainable artificial intelligence an overview for clinical nurses.pdf - Accepted Version Restricted to Repository staff only until 10 September 2025. Download (2MB)

Abstract

Artificial intelligence (AI) is transforming healthcare by enhancing clinical decision-making, particularly in nursing, where it supports tasks such as diagnostics, risk assessments, and care planning. However, the integration of non-explainable AI (NXAI) – which operates without fully transparent, interpretable mechanisms – presents ethical challenges related to accountability, autonomy, and trust. While explainable AI (XAI) aligns well with nursing's bioethical principles by fostering transparency and patient trust, NXAI's complexity offers distinct advantages in predictive accuracy and efficiency. This article explores the ethical tensions between XAI and NXAI in nursing, advocating a balanced approach that emphasises outcome validation, shared accountability, and clear communication with patients. By focusing on patient-centred, ethically sound frameworks, it is argued that nurses can integrate NXAI into practice, addressing challenges and preserving core nursing values in a rapidly evolving digital landscape.

Item Type:	Article
Additional Information:	This document is the Accepted Manuscript version of a Published Work that appeared in final form in British Journal of Nursing, copyright © MA Healthcare, after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.12968/bjon.2024.0394.
Uncontrolled Keywords:	1110 Nursing; 4205 Nursing
Subjects:	B Philosophy. Psychology. Religion > BJ Ethics R Medicine > RT Nursing T Technology > T Technology (General)
Divisions:	Nursing and Advanced Practice
Publisher:	MA Healthcare
SWORD Depositor:	A Symplectic
Date Deposited:	11 Mar 2025 09:22
Last Modified:	11 Mar 2025 09:30
DOI or ID number:	10.12968/bjon.2024.0394
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25843

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