JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is rapidly transforming healthcare, offering significant advancements in patient care, clinical workflows, and nursing education. While AI has the potential to enhance health outcomes and operational efficiency, its integration into nursing practice and education raises critical ethical, social, and educational challenges that must be addressed to ensure responsible and equitable adoption.

Objective:

This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing.

Methods:

A comprehensive literature search was conducted in PubMed, CINAHL, Web of Science, Embase, and IEEE Xplore to identify relevant review articles (systematic, scoping, narrative, etc.) on AI integration in nursing, published up to October 2024 (with an updated search in January 2025). Eligibility was determined using the SPIDER framework to include reviews addressing AI in any nursing context (practice or education). Two reviewers independently screened studies, extracted data, and assessed the quality of each review using ROBIS and an adapted AMSTAR 2 tool. The findings were synthesized using thematic analysis to identify key recurring themes across the included studies.

Results:

Eighteen reviews met the inclusion criteria, encompassing diverse nursing domains (clinical practice, education, and research). Three overarching themes emerged: (1) Ethical and Social Implications – widespread concerns about data privacy, algorithmic bias, transparency in AI decision-making, accountability, and equitable access; (2) Transformation of Nursing Education – the need for curriculum reform to integrate AI literacy, the use of AI-driven educational tools, and training to address ethical and interpersonal skills in an AI-enabled environment; and (3) Strategies for Integration – the importance of scalable implementation plans, development of ethical governance frameworks, promoting equity in AI access, and fostering interdisciplinary collaboration. Critical barriers identified across studies include algorithmic bias, data privacy concerns, resistance to AI adoption among nursing professionals, lack of standardized AI education (highlighting the need for curriculum updates), and disparities in access to AI technologies.

Conclusions:

AI offers significant promise to transform nursing practice and education, but realizing these benefits requires proactive strategies to address the identified challenges. This review recommends implementing robust ethical AI governance frameworks and regulatory guidelines, integrating AI literacy and ethics into nursing curricula, and encouraging interdisciplinary collaboration between healthcare and technology professionals. Such measures will help ensure that AI technologies are adopted in nursing practice in an ethical and equitable manner. Further research is needed to develop standardized implementation strategies and to evaluate the long-term impacts of AI integration on patient care and professional nursing practice.