JMIR Publications

ABSTRACT

Interest in physical AI and robotics in health care is increasing, but the nursing literature shows 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 (VR) 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 sim-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 five linked elements and supported by a four-layer technical architecture that specifies an implementable architecture and pipelines. Four key propositions ground the framework: (1) nursing robot training should be competency formation, not 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) sim-to-real transfer should be governed by explicit nursing-relevant validation criteria and retained human accountability. The proposed nurse-in-the-loop, site-specific VR framework is more appropriate for nursing deployment than a purely general-purpose physical AI pipeline. A detailed technical architecture is provided as a Multimedia Appendix to illustrate implementation feasibility and support future prototype development and empirical evaluation.