Authors
Seungman Kim
Published in
JMIR nursing. Volume 9. Pages e96848. Oct 05, 2026. Epub Oct 05, 2026.
Abstract
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.
PMID:
42832750
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.
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