Before an AI tool reaches a clinic, it goes through steps that help show whether it might work safely in real settings. After all, would you bring a tool into your clinic without giving it a fair test first?
A clear job to focus on
A reliable tool has one specific purpose — nothing vague. If it claims it can do everything, shouldn’t we pause and ask, “Can any tool really do that?”
Learning from many types of images
AI learns from examples. If it only studies one clinic’s images, how well would it handle pictures from somewhere else? That’s why variety might make it more steady.
Company testing: a first glance
Developers test the tool on new images. Useful? Yes. But is this enough to know how it performs in real life? Not quite.
Independent testing: the real-world check
When a tool is tested by new teams with new patients, performance often changes. Isn’t that exactly the kind of information we need before trusting it?
Regulatory review
In the U.S., many tools go through the FDA. These reviews may help make sure the tool stays within its limits and is described clearly. After all, who wants unclear instructions in a clinic?
Ongoing monitoring
AI can change over time. If the world around it changes, shouldn’t we check back in to make sure it still works as expected?
AAAI-D doesn’t approve tools — it simply helps people understand how they were tested so they can make their own thoughtful decisions.