AI is being rapidly integrated into healthcare, and its role is expanding quickly. Yet, despite this rapid adoption, AI trust among patients remains critically low. That is a problem because trust is fundamental to healthcare. Patients who trust their healthcare providers are more likely to share information, follow recommendations, and actively engage with their care. In my current professional role, I spend a considerable amount of time thinking about the reasons why patients should trust AI-driven healthcare technology.

There are several possible reasons driving AI distrust. One widely cited barrier to trust is explainability: How does AI reach its conclusions? If a patient cannot understand how and why an AI system arrived at an answer, many people argue patients will likely have skepticism about trusting it. 

But, does understanding how something works really determine whether we trust it? Patients (and, if I am being honest, many doctors too) do not understand how an MRI machine works. They do not understand the physics behind magnetic resonance or how the machine transforms signals into an image. Yet, we use MRIs every day because we have learned they are reliable and produce valuable results. Our trust does not come from understanding the technology. It comes from years of experience, validation, and consistent performance.

AI may have to earn trust in the same way. Patients are likely to care less about the technical architecture of an AI model than whether it performs reliably and produces meaningful results. If an AI system consistently does what it is supposed to do, and if those results are independently validated, understanding exactly how it reached its conclusion may become less important for many patients and their loved ones.

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