Where general AI fails on specialized dental questions

General language models are good at sounding like the literature. They are much less good at being the literature, and in dentistry the gap shows up in specific, checkable ways.

Ask about a material class and the answer will be broadly right. Ask about a named product, a specific standard, or a result from the last two years, and the answer is often confidently wrong. Fluency is not evidence of retrieval.

The failure that matters most is silent. A model that cannot find a source will produce an answer anyway, and nothing in the response distinguishes that case from a well-supported one.

The fix is architectural rather than conversational. Answers should be generated against a retrieved set of sources, and the sources should be shown. That is the design principle behind Ask Dentistry, and it is why it returns references you can open rather than assertions you have to trust.