9 Actions Health Systems Can Take to Close the AI Trust Gap

Radiology has no shortage of AI tools, but what’s frequently missing is the reliable governance needed to use them safely and consistently. This trust gap spans clinicians, patients and executives alike, yet the root issue is often the same: without clarity, AI can feel like a gamble.

The AI Governance in Healthcare eBook introduces a five-step framework that helps leaders align strategy with real-world practice so AI adoption becomes faster, safer and more transparent. Before diving into the full document, here are nine practical steps health systems can implement today to strengthen trust in radiology AI.

1. Establish an AI Governance Committee

Bring together the clinical champion, IT, legal, quality and executive sponsor. Assign clear roles and responsibilities, decision rights and accountability.

2. Have a Consistent Initial Intake and Evaluation Process 

Ask vendors for an applied model card, a document that clearly states how an AI model performs within your defined use case, not just in the abstract.

3. Create an AI Governance Framework Before You Evaluate Anything 

Use structured intake criteria, safety screens and workflow fit assessments before a tool ever reaches the pre-implementation stage.

4. Teach Clinicians How to Recognize Automation Bias

Build education into governance. Use real examples and evidence to highlight how easy it is to over-trust AI. This type of awareness can help prevent over-reliance.

5. Integrate AI Into Workflow With the Least Possible Friction

Before deciding on a go-forward plan, assess if a solution adds clicks, alerts or more cognitive load. If it does, clinicians will (and should) ignore it.

6. Build a Monitoring Dashboard Before You Deploy

Track real-world sensitivity/specificity, alert volume and override rates, data drift, safety events and user adoption from day one. 

7. Use Tiered Transparency With Patients

Patients don’t need dense technical detail, but they need clarity, honesty and the ability to opt out AI being part of their treatment plan.

8. Treat Governance as a Cycle, Not a Project

This is a big mindset shift. Every AI initiative should go through Assess → Define → Select → Execute → Monitor → Repeat. Each cycle makes the next deployment easier, safer and more trusted.

9. Performance Must Use Your Own Data, Not the Theoretical Studies/Data

Local performance is the only performance that matters. Require every vendor to monitor model performance in your system, your protocols and your patient population after going live.

Closing the Trust Gap

The trust gap in radiology AI can feel overwhelming because it comes from so many directions, but trust doesn’t improve through wishful thinking – it improves through structure. Governance is that structure.

If you’re ready to move forward, the AI Governance in Healthcare: A 5-Step Framework for Leaders is a great next step. It offers the templates, checklists and decision criteria that address the points above and allows you to operationalize trust.