Building Trust in AI: The Human Element

Governance provides structure, but adoption happens when technology aligns with clinicians’ values and furthers efficiency. 

Healthcare professionals across every specialty take pride in accuracy, quality and patient outcomes. When AI tools are tailored to support those priorities, they strengthen – not threaten – clinical expertise.

“Clinicians will trust AI when it helps them deliver their best work.” — Ainsley MacLean, MD, FACR, Founding Partner at Ainsley Advisory Group

Implementation is one of the most important touchpoints before and after adoption. It has to be thoughtful, collaborative and grounded in a clear understanding of how technology supports current workflows.

Younger physicians may adopt AI tools more quickly, while experienced clinicians often want clear evidence that these solutions enhance care quality and safety. The key for both is the same: show tangible value in the outcomes that matter most to them and their patients.

Questions to Help Establish Trust in AI

The next layer of clinician adoption and trust involves demonstrating that AI delivers value where it matters most. To address this, leaders must answer questions such as: 

• How consistently does it support clinical decision-making?
• Does it meaningfully reduce workload or turnaround time?
• Does it integrate cleanly into the surrounding workflow?
• Does it introduce variation across sites or populations?

As clinicians’ trust in AI grows, leaders must ensure tools deliver reliable outcomes across the entire system. Governance must move beyond individual tools to provide systemwide oversight and ensure consistent support for care.

Ainsley MacLean, MD, FACR, a Founding Partner at Ainsley Advisory Group, shared in a recent webinar that ensuring consistent outcomes across systems is a defining challenge of AI governance. This is a responsibility no human team can shoulder alone, especially when adoption levels and user comfort vary widely from site to site and across different care environments.

“I don't think a human being can monitor these tools, monitor for drift. There’s a lot of variation when you look across patient populations. For large systems like LucidHealth and Yale, the performance of an algorithm may differ between centers, one center being more urban and another more suburban. I think the C-suite should be able to see how the solution is performing. What different results are we seeing? And then learn from that and then optimize the model,” said Dr. MacLean.

Leadership’s Role in AI as a Systemwide Capability

With AI now influencing decisions across multiple layers of the organization, its oversight extends beyond model scrutiny and simple performance monitoring. At this point, AI becomes a capability that leaders must understand deeply to guide care delivery and sustainable healthcare models. As Dr. MacLean explained, "AI isn’t a tool; it’s a capability. Every executive leader needs to understand it, because it will shape how we work across every part of the system."

Governance as the Engine of Scalable AI

Health systems advancing with AI recognize that robust governance is essential. Treating governance as a core discipline enables organizations to build the necessary infrastructure, earn clinician trust, ensure consistency across sites and keep patient outcomes at the center, ultimately driving scalable and reliable adoption.

If your team is working through how to operationalize AI safely and at scale, access the full webinar for deeper insights from LucidHealth and Yale New Haven Health on building a successful, scalable AI strategy.