Artificial Intelligence in Family Nursing: An Evidence‐Informed Policy Framework for Safe and Equitable Integration

Vollständiger Abstract

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ABSTRACT Aim To propose an evidence‐informed nursing policy framework for the safe, equitable and human‐centred integration of artificial intelligence into family nursing. Background Ageing populations, chronic illness and the shift towards home‐ and community‐based care have increased reliance on families and family nursing. Artificial intelligence is increasingly used in healthcare for prediction, decision support, communication, remote monitoring and documentation. However, its implications for family nursing remain underexamined because family nursing involves relationships, shared decision‐making and family‐level information. Sources of Evidence Academic and policy literature on artificial intelligence, digital health, family nursing, ethics, regulation, workforce development and implementation was reviewed in a structured manner and narratively synthesised to identify policy‐relevant opportunities, risks and requirements. Discussion Artificial intelligence may help nurses identify risks earlier, tailor education and referrals, extend routine support through digital tools and evaluate family‐level outcomes. These opportunities must be balanced with concerns about privacy, consent, bias, digital exclusion, accountability and over‐reliance on automated recommendations. Japan is used as an illustrative context because rapid population ageing, strong family involvement and community‐based integrated care highlight issues relevant to many health systems. Conclusion Safe integration requires five linked policy domains: ethical and data governance, workforce development, interdisciplinary co‐design, adaptive regulation and accountability, and sustainable funding and reimbursement. Implications for Nursing Practice Nurses should use artificial intelligence–supported recommendations as prompts for professional assessment, not as replacements for relational judgement, family communication or human follow‐up. Implications for Nursing Policy Policymakers, regulators, educators and health organisations should clarify accountability, protect family‐level data, embed artificial intelligence literacy in nursing education, require inclusive design and support reimbursement for nurse‐led, technology‐enabled family care.