Exploring the ethical dimensions of AI integration in nursing practice: A systematic review

IF 6.3 4区医学 Q1 NURSING

Journal of Nursing Regulation Pub Date : 2025-10-01 DOI:10.1016/j.jnr.2025.08.001

Khaldoon Aied Alnawafleh RN, MSN, PhD , Wesam Taher Almagharbeh RN, MSN, PhD , Hazem AbdulKareem Alfanash RN, MSN, PhD , Amal Ali Alasmari RN, MSN, PhD (Assistant Professor) , Amal Ali Alharbi RN, MSN, PhD , Mashael Hasan Alamrani RN, MSN, PhD , Sameer A. Alkubati RN, MSN, PhD , Malik A. Altayar PhD , Khulud Ahmad Rezq RN, MSN, PhD

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引用次数: 0

Abstract

Background

Artificial intelligence (AI) is being increasingly integrated into nursing practice, offering tools such as clinical decision support systems, predictive analytics, and robotic aids. While these technologies promise greater efficiency and precision, they also raise complex ethical challenges specific to the relational, advocacy-driven nature of nursing.

Purpose

To systematically review and synthesize the ethical implications of AI integration in nursing practice, focusing on five key domains: patient autonomy, privacy, accountability, equity and algorithmic bias, and nurse–patient relationships.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines, a systematic review was conducted using PubMed, CINAHL, IEEE Xplore, and Google Scholar. Studies published between 2018 and 2025 that addressed AI ethics within nursing were included. Data were extracted from included studies and analyzed through thematic synthesis.

Results

Thirty-three articles met the inclusion criteria. Patient autonomy (67%), privacy (61%), and accountability (49%) were the most frequently discussed ethical concerns. AI’s opacity often hindered informed consent and shared decision-making. Privacy risks included secondary data use and insufficient data governance. Accountability remained diffuse in cases of AI error, with nurses caught between professional duty and opaque algorithmic suggestions. Equity and algorithmic bias issues emerged in 42% of studies, especially when AI was trained on nondiverse datasets. Finally, nurse–patient relationships were strained in settings where AI mediated or replaced human contact, particularly in elder care.

Conclusion

Ethical integration of AI in nursing requires nurse-centered system design, transparent governance protocols, and ethical education. Future efforts must emphasize equitable data practices, clarify liability, and preserve the relational foundation of nursing.

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探索人工智能在护理实践中的伦理维度：一项系统综述

人工智能（AI）正越来越多地融入护理实践，提供临床决策支持系统、预测分析和机器人辅助等工具。虽然这些技术承诺更高的效率和精度，但它们也提出了复杂的伦理挑战，具体到护理的关系，倡导驱动的性质。目的系统回顾和综合人工智能在护理实践中的伦理影响，重点关注五个关键领域：患者自主、隐私、问责、公平和算法偏见以及护患关系。方法遵循系统评价和元分析2020指南的首选报告项目，使用PubMed、CINAHL、IEEE explore和谷歌Scholar进行系统评价。2018年至2025年间发表的关于护理中人工智能伦理的研究也被纳入其中。从纳入的研究中提取数据，并通过专题综合进行分析。结果33篇文章符合纳入标准。患者自主权（67%）、隐私（61%）和责任（49%）是最常讨论的伦理问题。人工智能的不透明性经常阻碍知情同意和共同决策。隐私风险包括二次数据使用和数据治理不足。在人工智能出错的情况下，问责制仍然很分散，护士在专业职责和不透明的算法建议之间进退两难。在42%的研究中出现了公平性和算法偏差问题，特别是当人工智能在非多样化数据集上进行训练时。最后，在人工智能介导或取代人类接触的环境中，特别是在老年人护理中，护患关系变得紧张。结论人工智能在护理中的伦理整合需要以护士为中心的系统设计、透明的治理协议和伦理教育。未来的努力必须强调公平的数据实践，明确责任，并保持护理的关系基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Nursing Regulation NURSING-

CiteScore

4.60

自引率

12.50%

发文量

审稿时长

54 days

期刊介绍： Journal of Nursing Regulation (JNR), the official journal of the National Council of State Boards of Nursing (NCSBN®), is a quarterly, peer-reviewed, academic and professional journal. It publishes scholarly articles that advance the science of nursing regulation, promote the mission and vision of NCSBN, and enhance communication and collaboration among nurse regulators, educators, practitioners, and the scientific community. The journal supports evidence-based regulation, addresses issues related to patient safety, and highlights current nursing regulatory issues, programs, and projects in both the United States and the international community. In publishing JNR, NCSBN''s goal is to develop and share knowledge related to nursing and other healthcare regulation across continents and to promote a greater awareness of regulatory issues among all nurses.