RAG Systems Require Ethical Governance in Nursing

In a 2026 viewpoint published in JMIR Medical Informatics, Xinyi Tu et al. analyze ethical imperatives for implementing retrieval-augmented generation (RAG) systems in clinical nursing. They identify risks—bias, data quality and provenance issues, privacy concerns, and LLM opacity—and propose safeguards including robust data governance, explainable AI, continuous monitoring, and sustained human oversight. The authors call for collaboration among clinicians, AI developers, and policymakers to protect patient safety and equity.