ReviewInternational nursing review2026
Artificial Intelligence in Family Nursing: An Evidence-Informed Policy Framework for Safe and Equitable Integration.
Review in International nursing review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo propose an evidence-informed nursing policy framework for the safe, equitable and human-centred integration of artificial intelligence into family nursing.
backgroundAgeing 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.
conclusionSafe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.