Generative AI Needs a Pilot: Positioning Advanced Practice Nurses as the "Human-in-the-Loop" for Primary Care Safety

Generative artificial intelligence (GenAI) is increasingly integrated into primary care, supporting documentation, clinical summaries, and patient-facing communication. While its potential to reduce cognitive workload is widely recognized, GenAI introduces a safety paradox: as outputs become more fluent, inaccuracies such as hallucinations and omissions may become harder to detect. This creates a largely unrecognized burden in routine care, referred to as the "verification tax," which risks redistributing rather than alleviating clinician workload. A central question is not whether GenAI is sufficiently accurate, but how its outputs are incorporated into clinical workflows and safety structures. In this commentary, we propose a human-in-the-loop (HITL) model that positions advanced practice nurses (APNs) as first-pass reviewers for high-volume, context-sensitive GenAI outputs. This role focuses on routing, contextualization, and verification, ensuring that machine-generated content is appropriately interpreted before influencing patient care. Given their roles in patient education, care coordination, and longitudinal management, APNs may be well-suited to address the gap between standardized model outputs and individualized clinical contexts. A conservative routing architecture and minimum governance framework are outlined, including explicit safety boundaries, structured escalation pathways, and the need for protected time for verification work. Potential limitations include the risk of redistributing invisible labor, model and workflow drift, and equity concerns in patient-facing communication. These considerations suggest that appropriate resourcing, audit mechanisms, and role-based governance are necessary. Positioning APNs as first-pass HITL reviewers may offer a pragmatic approach to integrating GenAI into primary care while supporting patient safety and interprofessional collaboration.