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Related Experiment Videos

Trust, Verify, Override: Behavioral Governance for Generative Artificial Intelligence in Medical Imaging.

Jeffry Glenning¹, Lisa Gualtieri²

¹Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Health Education & Behavior : the Official Publication of the Society for Public Health Education

|June 1, 2026

Summary

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Issues And Trends In Healthcare Delivery System

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Cost Containment
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Generative artificial intelligence (AI) in clinical settings requires robust governance beyond initial validation. This framework focuses on clinician behaviors like trust, verification, and override to ensure safe and equitable AI adoption in healthcare.

Area of Science:

Clinical Informatics
Health Behavior Science
Artificial Intelligence in Medicine

Background:

Generative AI in clinical settings, including medical imaging, introduces behavior-mediated safety challenges.
Current guidance primarily addresses predeployment validation, lacking specificity for postdeployment governance within daily workflows.

Purpose of the Study:

To propose a practical framework for governing generative AI in clinical workflows, focusing on postdeployment behavior.
To address safety and equity concerns related to AI-generated content in clinical communication and patient explanations.

Main Methods:

Synthesizing evidence on AI failure modes, automation bias, and implementation monitoring.
Developing a framework based on behavior change and implementation science principles.

Keywords:

automation bias clinical documentation generative artificial intelligence governance implementation science medical imaging

Related Experiment Videos

Translating postdeployment risks into stakeholder-specific interventions.

Main Results:

A framework is proposed with three target behaviors: trust (transparent scope), verify (cross-checks), and override (documented corrections).
Interventions include competency-based education, equity-stratified monitoring, and rollback procedures.
The framework addresses safeguarding patient comprehension and autonomy with AI-generated explanations.

Conclusions:

Effective postdeployment governance of generative AI is crucial for safe and equitable healthcare adoption.
Viewing AI governance as a health education and behavior challenge is essential.
The proposed framework provides actionable strategies for managing AI risks in clinical practice.