Artificial Intelligence in Family Nursing: An Evidence-Informed Policy Framework for Safe and Equitable Integration.
Honda Junko, Itoh Sakiko, Kubota Kazumi
PMID 42702934WHAT IT FOUND
Nurses should use artificial intelligence outputs as prompts for assessment and conversation, not as decisions.
This policy framework is not tested evidence that the tools improve family care.
Key findings
01The article proposes four opportunity areas for artificial intelligence in family nursing: proactive risk identification, personalised intervention design, extended digital support and enhanced evaluation of family-level outcomes.
02It proposes five policy domains for safe and equitable integration: ethical and data governance, workforce development, interdisciplinary co-design, adaptive regulation and accountability, and sustainable funding and reimbursement.
03The framework is author-proposed and evidence-informed, not empirically validated or internationally endorsed.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
It was not a systematic review, meta-analysis or formal evidence map, and it did not include exhaustive evidence mapping or formal quality appraisal. Direct empirical evidence on artificial intelligence in family nursing is limited, and some implications were drawn from adjacent fields. Japan was used as an illustrative context, not a universal model, and the framework has not been empirically evaluated.
Declared interests
The authors report no conflicts of interest and nothing to report. They state that artificial intelligence-assisted tools were used only for language editing, grammar checking and clarity, not for research questions, analysis, framework development, evidence interpretation or conclusions.
The easy way to misread this
Do not read this as evidence that artificial intelligence improves family nursing outcomes. It is an author-proposed policy framework, not an empirically validated model.