Most health AI is built first, shown to clinicians later, and then rolled out with a request to trust it. This panel makes the case that trust is decided much earlier, when someone chooses who validates the training data and checks the model’s reasoning. Hear what changes when clinicians help build the tool from the start, and what health systems should ask vendors before they adopt.

In this webinar, we’ll cover:

• Why “we tested it with clinicians” and “clinicians helped build it” are different claims, and only one earns lasting trust
• What changes in a model’s real-world reliability when clinicians validate it during development, not after
• The gap between a clinician who helped build a tool and a frontline clinician who is handed it and told to trust it
• Why liability concerns get easier to resolve when clinical judgment is built in from the start
• What health systems should ask AI vendors before adopting, starting with “who validated this, and when”