JMIR Nursing

AI has supported nursing and health care for years through decision support, prediction, monitoring, and workflow tools; what is changing is the breadth and proximity of AI-generated information to everyday care. Existing nursing AI literature has advanced AI literacy, safety, ethics, and governance, yet less attention has focused on how AI-mediated care reshapes contextual nursing judgment, patient recognition, professional behavior, and relational accountability at the point of care. Nursing digital competence may therefore increasingly need to extend beyond AI literacy toward humanistic meaning-making and behavioral governance. We offer MESH as a provisional integrative nursing lens with 4 domains: meaning-making, ethical presence, system and relational workflow governance, and human-in-the-relationship. Standardized nursing data are considered one enabling bridge through which nursing-sensitive information may remain visible within AI-supported decision environments. The MESH-action cycle—recognize, interpret, communicate, govern, and evaluate—is proposed as a 5-step practice-oriented pathway for applying these domains. MESH is not a validated competency model; its purpose is to support inquiry, education, implementation design, and empirical testing of whether AI augments or obscures nursing judgment and patient recognition.

Socially assistive robots are increasingly explored in health care, but evidence from routine pediatric outpatient settings remains limited.

The rapid integration of generative AI (GenAI) into nursing education has prompted widespread concern about academic integrity and assessment validity. However, this dominant discourse may obscure a more fundamental challenge: preparing nursing students for clinical practice environments where AI already mediates triage, diagnostics, documentation, and clinical decision-making.

The rapid progress in digital transformation and the increasing reliance on AI in health care have created an urgent need for validated assessments of digital and AI literacy among nursing students. However, there is a substantial gap in culturally adapted assessment tools for Arabic-speaking populations.

Daytime urinary incontinence is a common childhood condition that can persist into adolescence, and it is associated with psychological distress, social and educational difficulties, and an increased risk of later mental disorders. The co-designed smartphone app “URApp” was developed in partnership with young people aged 10 to 18 years to support their self-management of bladder symptoms, but it is not yet used by community pediatric continence clinics to facilitate ongoing patient bladder training.

The rapid growth of social media has transformed communication, with more than 5.66 billion users worldwide in 2025 who spent an average of 141 minutes daily on platforms such as Facebook, Instagram, WhatsApp, YouTube, and TikTok. Nurses frequently use social media to exchange work-related information, communicate with others, and relieve stress. While social media can support professional development, excessive or inappropriate use at work may compromise patient care. Despite widespread use, research on nurses’ demographic factors and actual workplace social media behaviors remains limited.

Recent advancements in digital health technologies have led to the accumulation of large volumes of unstructured text data, particularly in electronic health records. Given that nursing documentation frequently includes detailed descriptions of daily living activities and psychosocial contexts, existing medical natural language processing (NLP) tools may not be directly applicable without adaptation. Moreover, the integration of standardized nursing terminologies into NLP systems remains limited. Although NLP has the potential to support risk detection, information management, and care planning, its practical applications in nursing have not yet been systematically mapped.

Clinical handover is the process during which responsibility and accountability for care are transferred between clinicians. AI has the potential to improve the reliability and completeness of clinical handover by helping clinicians detect predefined content areas that have been communicated, identify explicit information gaps, and prompt clarification before responsibility is transferred.

Interest in physical AI and robotics in health care is increasing, but the nursing literature shows that the evidence base remains early, nurse-centered applications are underdeveloped, and real-world experiential evidence is limited. Nurses are more likely to accept robots that reduce physically demanding and repetitive work while preserving the interpersonal and judgment-intensive core of nursing practice. This conceptual paper proposes a nursing-centered framework in which virtual reality functions not merely as a simulator but as a scaffolded training infrastructure for supportive physical AI systems, enabling nurse augmentation, site-specific adaptation through digital twins, and staged simulation-to-real transfer. The framework was developed through a conceptually integrative and implementation-aware synthesis drawing on nursing robotics, AI in nursing, immersive simulation, digital twins, human-in-the-loop learning, and physical AI development literature. It is organized around 5 linked elements and supported by a 4-layer technical architecture that outlines functional requirements and implementation pathways. Four key propositions ground the framework: (1) nursing robot training should focus on competency formation, not on decontextualized data accumulation; (2) training should proceed through progressive fidelity and staged autonomy; (3) digital twins should function as operational bridges for local ward adaptation; and (4) simulation-to-real transfer should be governed by explicit nursing-relevant validation criteria and retained human accountability. The proposed nurse-in-the-loop, site-specific virtual reality framework offers a nursing-centered complement to general-purpose physical AI pipelines by making workflow fit, role boundaries, local adaptation, and governed transfer explicit design requirements.

Technologies such as virtual reality (VR) and augmented reality (AR) have been increasingly incorporated into nursing education to support the development of cognitive, psychomotor, and behavioral competencies. In midwifery, immersive environments offer opportunities to simulate high-risk or low-frequency clinical scenarios, strengthening students’ confidence and preparedness for professional practice. However, there is still significant variability in how VR and AR are pedagogically implemented, with limited understanding of their theoretical grounding, instructional design, and educational outcomes.

AI is rapidly transforming clinical nursing, promising administrative relief and decision support. However, the frontline reality presents a double-edged sword effect, where technological empowerment is frequently offset by novel occupational burdens and technostress.

Gaps in knowledge, inhalation skills, and adherence continue to hinder effective chronic obstructive pulmonary disease (COPD) management.