Is Your Dental Practice Showing Up in ChatGPT and AI Search?

What we have measured, and what we have not

The figures below come from AIOInsights research: 466 public websites we chose to evaluate, scored on six pillars from what a visitor, a crawler or an answer engine can read from the public web. Nobody asked us to evaluate them and none are our clients. Every individual evaluation is published at public signal research, and the corpus-wide findings are written up in AI Trust Signals: What 466 Websites Show.

We have not yet evaluated a cohort of dental practices. That is stated plainly because the alternative is implying a dental benchmark we do not hold, on a page whose whole subject is what a machine can verify. What we can report is the corpus-wide pattern, and it is consistent enough that it is very unlikely dentistry is the exception.

Across all 466 sites:

Restricted to the 168 businesses in the corpus that actually have a service area, the kind a dental practice is, local presence averages 4.53 and reputation 2.79. Those are the two numbers a practice should expect to be weakest on.

"Find me a dentist" is now a question with one answer

A search results page offers ten options and lets the patient choose. An assistant asked to recommend a dentist near a named place returns a short list, often three names, sometimes one. There is no page two.

That changes what matters. Being findable was enough when the patient did the choosing. Being chosen requires the model to have a specific, corroborated reason to name your practice over the one four blocks away that looks identical to it.

For a practice, the signals that supply that reason are unusually concrete: consistent location and hours everywhere they appear, named dentists with credentials, the specific procedures offered written out rather than implied, insurance and payment reality stated plainly, and independent local presence that agrees with all of it.

Why a good practice gets overlooked

Clinical quality is invisible to a machine. So is a warm front desk, a gentle hygienist and twenty years of loyal patients. None of that is legible, and a practice that is genuinely excellent can be entirely absent from the answer.

The failures we see most often across service-area businesses in the corpus are dull and fixable:

• The address disagrees with itself. A suite number on one listing and not another is enough to make an engine uncertain it is the same practice.
• Procedures are implied, not stated. "Cosmetic dentistry" does not answer "who does same day crowns near me".
• The dentists are anonymous. A practice with no named clinician is harder to verify than one with a single named dentist and a credential.
• Insurance is left vague, which is one of the most common things a patient asks an assistant before anything clinical.

What to do, in order

1. Measure your own practice rather than assuming the corpus applies to you. The free check returns your six pillar scores.
2. Make the location and hours identical everywhere. Cheapest work with the clearest effect, and it is the weakest pillar for service-area businesses after reputation.
3. Write the procedures out in the words patients use, including what you do not do.
4. Name the clinicians, with credentials.
5. Then reputation, which is the slow one and the one that never finishes.

Run my free check How it is scored

Limits of this data

• No dental cohort has been evaluated. Every figure on this page is corpus-wide or from the service-area subset, and is offered as the pattern to expect, not as a dental benchmark. When a dental cohort exists it will be published at public signal research like every other.
• These are sites we chose, not a survey. The cohorts are the firms we evaluated during competitor research. No figure here should be read as "the average firm" in any field.
• A score measures published signals on a date. It is not a judgment of the practice. A firm with an excellent reputation and no public trace of it scores low here, correctly, because the question being asked is what a machine can verify.
• We have not shown that these signals cause an AI recommendation. This reports what sites carry. Our share of voice research measures what engines actually answer. Connecting the two properly needs both, over time.
• Nothing here is clinical or regulatory advice.