JMIR Publications

ABSTRACT

High-fidelity AI-generated video is rapidly entering patient education and clinical communication across portals, telehealth workflows, and social platforms, but operational governance often lags behind real-world deployment and iterative system change. We propose a Risk–Ethics Matrix that combines residual clinical risk (likelihood × severity after mitigations) with an Ethical Alignment Score that operationalizes autonomy, beneficence, nonmaleficence, and justice to yield actionable dispositions (encourage, permit with oversight, restrict/redesign, or prohibit). The framework links each disposition to dossier-based review, minimum controls, and postdeployment monitoring triggers—focused on measurable outcomes (eg, comprehension, content-attributable follow-up burden, incidents/complaints, and equity gaps) as well as provenance and change control—to support auditable, revisitable decisions over the system life cycle.