Generative artificial intelligence (AI) in clinical communication, including in medical imaging, presents behavior-mediated safety challenges: outcomes depend on how clinicians verify AI-generated content under time constraints. However, guidance has largely focused on predeployment validation, with less specificity about postdeployment governance in day-to-day workflows. This perspective synthesizes evidence on failure modes, automation bias, and implementation monitoring and proposes a practical framework organized around three target behaviors: trust (transparent scope limits), verify (structured cross-checks against source data), override (documented corrections that become learning signals). Drawing on behavior change and implementation science, we translate postdeployment risks into stakeholder-specific interventions, including competency-based education, equity-stratified monitoring with prespecified triggers for fairness drift, and rollback procedures. The framework extends to patient-facing AI-generated explanations, where comprehension and autonomy must be safeguarded. This approach positions governance as a health education and behavior challenge essential for safe, equitable adoption.
Glenning et al. (Mon,) studied this question.