AI in nursing: Transforming practice, education and workflows
Artificial intelligence (AI) is rapidly reshaping the nursing profession, offering promising advancements across education, clinical care, and operational management. A recent integrative review of 25 studies highlights how AI is being integrated into nursing practice, revealing both the benefits and challenges associated with this integration.
education and training
AI-enhanced simulations and content creation tools are revolutionising nursing education. These technologies provide realistic clinical scenarios that improve student engagement, critical thinking, and case management skills. While learners reported increased satisfaction, their overall learning outcome did not change. AI-assisted tools also enhanced the clarity and quality of patient education materials, thereby fostering improved communication skills among nursing students.
clinical decision support and monitoring
AI-powered alert systems and wearable sensors are enabling earlier detection of patient deterioration, particularly in critical care settings. These tools facilitate timely interventions and reduce the need for ICU transfers. However, nurses emphasised the importance of maintaining professional judgement to avoid overreliance on automated insights. AI’s role in rehabilitation and postoperative care was also notable, with imaging tools and personalised follow-up pathways improving recovery outcomes and patient satisfaction.
workload and workflow management
AI is streamlining nursing workflows by automating routine tasks and predicting staffing needs. These efficiencies allow nurses to focus more on direct patient care, contributing to reduced burnout and improved morale. Predictive models based on electronic health records are helping allocate resources more effectively, while AI-supported discharge planning is enhancing follow-up care for high-risk patients.
professional perceptions
Overall, nurses and students view AI positively, recognising its potential to enhance efficiency and support clinical decision-making. Younger nurses were more receptive to AI, while concerns were raised about job displacement, depersonalised care, and erosion of critical thinking. Knowledge gaps and resistance to change continue to be barriers to widespread adoption. Interestingly male nurses were more receptive to use of AI than female nurses – factors such as perceived familiarity with the tools were provided as reasons for the differences.
ethical and safety considerations
The review identified significant ethical concerns, particularly around data privacy, algorithmic bias, and the preservation of human-centred care. Nurses advocated for robust governance frameworks, including regular audits of AI systems to ensure fairness and transparency. Maintaining empathy and the human touch in patient interactions was seen as essential, with AI positioned as a complement—not a replacement—for nursing expertise.
a strategic framework for integration
To guide the adoption of sustainable AI, the Nursing AI Integration Roadmap (NAIIR) was proposed. It outlines six key pillars: transformational education, advanced clinical integration, ethical governance, robust infrastructure, participatory design, and economic evaluation. This framework promotes a user-centric, ethically sound approach to AI in nursing.
AI offers transformative potential for nursing, enhancing education, clinical care, and operational efficiency. However, successful integration requires strategic planning, ethical safeguards, and ongoing professional development. By embracing AI as a supportive tool, the nursing profession can evolve while preserving its core values of compassion, critical thinking, and patient-centred care.
beyond nursing
The integration of artificial intelligence into nursing practice has broader implications for other health professions, including medicine, allied health, and pharmacy. Just as AI is enhancing clinical decision-making, education, and workflow efficiency in nursing, similar benefits could be realised across disciplines. For example, AI-driven diagnostic tools and predictive analytics can support physicians in early detection and personalised treatment planning. At the same time, allied health professionals may leverage AI for rehabilitation monitoring and patient engagement. The emphasis on ethical governance, professional judgement, and human-centred care in nursing also serves as a critical reminder for all health professions to adopt AI responsibly—ensuring that technology complements rather than replaces clinical expertise. Interdisciplinary collaboration will be essential to develop shared frameworks that promote safe, equitable, and effective AI integration across the healthcare system.
All accessed 12/09/2025:
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This blog includes content generated with the assistance of Copilot.