JMIR Nursing

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.

Older adults with diabetes are vulnerable, facing multimorbidity and challenges in reaching glycemic targets. When insulin therapy is required, limited knowledge of geriatric therapeutic goals often leads to regimens that increase the risk of hypoglycemia. Simplified approaches such as once-daily basal insulin are recommended, yet titration protocols and unclear regimens remain barriers in home care nursing. Digital decision support systems (DDSSs) such as GlucoTab address this gap by offering evidence-based titration guidance integrated into nursing workflows. DDSSs also strengthen nurse autonomy, which leads to improved care processes and better outcomes in home care settings.

Effective pain and opioid management education remains a persistent challenge in health professions training. Static case studies and lecture-based instruction are widely used but may be insufficient to develop the clinical reasoning and communication skills required in practice. Interactive virtual patient simulations, including those powered by large language models, offer scalable alternatives, but head-to-head comparisons of interactive and static case formats using identical content among interprofessional learner populations remain limited.

As patient populations become increasingly diverse, nurses must develop cultural competence to provide effective and equitable care.

Digital technology-based health care services like telemedicine and telenursing are expanding quickly globally. Although the use of telemedicine and telenursing has shown promise, there are a number of obstacles to overcome.

Machine learning (ML) has been demonstrated to enhance health care cost prediction by handling high-dimensional data and identifying complex patterns. However, current risk-adjustment models rarely incorporate structured nursing information derived from the nursing process. This information captures care needs and human responses to health problems.

Midwifery education in Nigeria is undergoing a transformation in pedagogical approach. Existing traditional teaching materials are being supplemented by digital technology-enhanced learning resources; however, educators’ limited pedagogical capacity may hinder their adoption. Thus, train-the-trainer (TTT) workshops were implemented to build capacity among midwifery educators to prepare them for the implementation of a digital learning platform across 20 institutions in Nigeria.

AI is increasingly being integrated into education and health care, offering opportunities to improve learning, understanding of clinical cases, and students’ self-confidence. However, it remains necessary to assess nursing students’ perceptions of AI and its impact on their academic and professional development.