aceintegration of AI technologies into nursing practice and education
Source article: The Role of AI in Nursing Education and Practice: Umbrella Review
BACKGROUND: Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. 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: We included systematic reviews, scoping reviews, rap…
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On March 12, 2025, an umbrella review in the Journal of Medical Internet Research synthesized 18 reviews from 274 screened records on AI in nursing. It found consistent reports of potential advances in patient care and clinical workflows alongside an urgent push to update nursing curricula with AI-driven tools and ethics training.
The findings matter because they frame AI adoption in nursing as both an educational and operational shift that requires new competencies, infrastructure, and governance. Uncertainty remains about long-term impacts on practice and patient outcomes, and about how to standardize implementation while addressing privacy, bias, and equitable access.
- Umbrella review screened 274 records and included 18 reviews of AI in nursing up to October 2024, covering professionals, students, educators, and researchers.
- Ethical and social implications were consistently highlighted across reviews, including data privacy, algorithmic bias, transparency, and accountability.
- Transformation of nursing education was identified as critical, requiring curricula updates with AI-driven educational tools and ethical decision-making skills.
- Authors call for scalable implementation models, ethical frameworks, interdisciplinary collaboration, and infrastructure investment to ensure equitable access.
AI integration can advance nursing by improving patient care and clinical workflows and transforming how nursing is taught and practiced.
AI adoption in nursing faces barriers including data privacy risks, algorithmic bias, lack of transparency and accountability, resistance to adoption, and disparities in access to AI technologies and standardized education.
The rundown
The review searched PubMed/MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore for systematic, scoping, rapid, narrative, and literature reviews and meta-analyses on AI in nursing, with risk of bias assessed using ROBIS and AMSTAR 2 adapted for different review types.
Synthesis identified three themes: persistent ethical and social concerns, urgent need to integrate AI literacy and AI-driven tools into nursing curricula, and need for robust ethical frameworks and interdisciplinary models to overcome resistance and access disparities.
Review findings are constrained by heterogeneity across included reviews and potential publication bias, limiting generalizability of conclusions about AI in nursing.
Sources
- Peer-reviewedJournal of Medical Internet Research2025-03-12
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