ChatGPT names the cosmetic dentist it is asked about in 42% of answers. Here is why it names the practice across town instead.

What 48 dental practices scored, counted from the audit records on 2026-10-02

Between June 27 and September 9, 2026 we ran the free check on 48 real U.S. dental practices in 18 metros: 19 cosmetic practices, 24 general practices and 5 family practices. Each check asked ChatGPT and Perplexity three patient-style questions about the practice's own city ("best [cosmetic] dentist in [city]", "I'm looking for a [cosmetic] dentist in [city]", "is [practice] a good choice?"), six answers per practice, 280 completed answers in all. A judge model marks whether the practice asked about is named in each answer. The tables are the count.

• Mean AI visibility score 22 of 100, median 17. One practice of 48 scored 60 or more (70, a cosmetic practice). Thirty-five of 48 sat at 17 or below.
• Cosmetic practices were named most, general and family practices least. Perplexity named the cosmetic practice asked about in 56.9% of answers, ChatGPT in 41.8%. For general dentists the two engines were level at about a third.
• One rival can own a metro. In Portland, OR, Timber Dental was named in 5 of the 6 Portland audits, 12 mentions in all. In Charlotte, NC, Charlotte Center for Cosmetic Dentistry and Charlotte Dental Arts were each named in 3 of the 5 Charlotte audits.
• 193 distinct rival practices were named across the 48 audits, 474 mentions in all. In 23 of 48 audits a single rival was named more often than the practice asked about. Ask twice and the shortlist reshuffles, which is why one screenshot proves nothing.
• Two practices were named in none of their six answers, one cosmetic practice in Tampa and one general practice in Portland.

Why dentistry is a distinct AI-visibility problem

Dental practices live in the same local-search reality as any "near me" category, but a few things make them their own case:

• The procedures are high-value, so each miss is expensive. A veneer case is $1,000–2,500 per tooth, Invisalign runs $4,000–7,000, a single implant $3,000–5,000. One AI-referred patient lost to the practice across town isn't a missed click — it's a five-figure treatment plan booked somewhere else.
• Patients ask by procedure, not just "dentist near me." "Best place for veneers in [city]," "who does clear aligners in [area]," "top cosmetic dentist for a smile makeover" — high-intent, high-margin questions where the assistant hands back a short, specific shortlist. If you're not on it, you never get the consult.
• Review consensus and directories decide it. For dentistry, AI engines weight third-party corroboration — Google, Yelp, Healthgrades, RealSelf, "best dentist in [city]" listicles — far above what your practice site claims about itself. If the practices named ahead of you own those pages, you lose the answer.
• Your Google rank doesn't save you. Plenty of practices rank on page one of Google and still score badly with AI. The engines don't read your SEO position; they synthesize from the sources above — a surface most practice owners and their marketing vendors have never looked at.

The upside hiding in the same data

Here's the part that should make a well-run practice lean in: the reason one practice in 48 cleared 60 is that almost nobody in dentistry is actively working this surface yet. The "best cosmetic dentist in {city}" slot is being decided by review aggregators and roundups — not by whoever has the biggest marketing budget — which means it's winnable by whichever practice the engines can most easily corroborate. That's not ad money; it's the directory, review, and roundup footprint the engines actually read. It's fixable, and most of your competitors aren't even watching it.

What "loses to a competitor" actually means for your practice

When an AI answer names three or four practices and yours isn't among them, that's not a ranking you slipped a spot in — it's a consult that went to someone else before the patient ever typed your name into Google. The patient asked an assistant for a recommendation, got a shortlist, and started booking. Increasingly people treat that shortlist the way they used to treat the top of the search results: as the answer. In a field where a single veneer case or Invisalign plan is worth thousands, being absent from the shortlist for your own city and procedure isn't a marketing nicety — it's high-value cases going to the practice the AI trusts instead.

The five things that actually move AI recommendations for dentists

In priority order — the work, not the theory. What follows are plausible levers, drawn from the sources the engines cite and from how retrieval works — hypotheses to test with a repeat measurement, not effects this audit has established. Structured data makes a page easier to read; it does not guarantee a citation or a recommendation.

1. 1Measure your real frequency first — don't guess from one answer. Ask ChatGPT and Perplexity buyer-style questions like your patients' several times and count how often you appear, and who's named instead. A single answer is noise (193 different rivals surfaced across our 48 audits). The honest baseline is a frequency, which is exactly what a check gives you.
2. 2Get into the third-party pages the AI already trusts. Being named in a "best cosmetic dentist / Invisalign provider in [city]" roundup, the health and local directories (Google, Yelp, Healthgrades, RealSelf), and cross-platform review pages plausibly does more than any claim on your own site, because those are the pages the engine retrieves and quotes — a hypothesis to test, not a measured effect.
3. 3Build consistent reviews across more than one platform. Cross-platform review presence — Google and Yelp and Healthgrades, not one perfect profile — is plausibly what these engines read as consensus for a local practice — a hypothesis, not an established effect. The offices that get named tend to be corroborated in several places, not just deep on one.
4. 4Publish answer-shaped, structured pages on your own site. Procedure and service-area pages with clear headings, plain factual statements, and Dentist/LocalBusiness structured data are easier for a retriever to extract and cite. Keep the facts (practice name, procedures, dentists, service area, phone) identical everywhere.
5. 5Keep one consistent story across every listing. Same practice name, same procedures, same location across your site, directories, and reviews. Contradictory facts are a plausible reason for an engine to hedge or pick a clearer entity — a hypothesis, and the cheapest one to remove.

Why the practice across town gets named and you don't

The skills overlap with SEO, but the target moved: from "rank on the results page" to "be the practice named inside the answer."

Why you can't fix what you haven't measured

Here's the trap that wastes the most effort: a single AI answer is not a measurement. These engines are nondeterministic — ask the same "best cosmetic dentist in [city]" question twice and you frequently get a different set of practices, because the live search returns slightly different pages each time (we have the run-to-run data). That's why 48 practices produced 193 distinct rival practices in the answers. So one screenshot with your name is false comfort, and one with a competitor is false panic. The only honest baseline is a frequency: across many runs, how often does the AI name you — and which practices does it name when it doesn't?

That's the measurement AskedAbout exists to give you. Start with a free 60-second check — your practice name, procedure focus, and city — and you'll see your AI Visibility Score (0–100), how often ChatGPT and Perplexity mention you, and which competing practices they name in your place. Then the $79 one-time Full Audit tells you which fixes matter most: 25 patient-intent questions across ChatGPT, Perplexity, Gemini, and Claude, sampled 3 times each — up to 300 API answers requested, with completed and failed counts shown — reporting your mention rate per engine, share-of-voice against the practices taking your slot, and the exact third-party sources the engines cite when they build a shortlist without you. One payment, no subscription.

A realistic timeline (so you don't quit a month in)

Moving how often you are named is a compounding effort, not a switch. A genuinely new source page — a fresh roundup mention, a corrected Healthgrades listing, a wave of new reviews — may start showing up in later samples once search indexes it, but building enough corroborated presence to be named often can take months, and no change guarantees it. The practices that win treat it like SEO: get an honest baseline now, change the inputs deliberately (the directories, the reviews, the consistent facts), and re-measure on a cadence and compare the counts. A before-and-after count cannot prove which change moved it. The ones that lose either never measure, or change one thing, screenshot it once, and give up while the practice across town keeps compounding.

Where AskedAbout fits

AskedAbout doesn't do the marketing for you — it tells you, specifically and in priority order, what to fix and what changed in a repeat of the same questions, across the four engine APIs we sample. The free check gives you a baseline in 60 seconds. The $79 audit turns "why doesn't AI recommend us?" into a short, ranked to-do list tied to your real mention frequency per engine and the actual sources each one cites about cosmetic dentistry in your city — so you have specific source pages and hypotheses to inspect before choosing changes. You hand the fix plan to whoever runs your marketing, do the work above, and re-check with the same questions to see whether the shortlist changed. Measure, fix, re-measure — with an honest number instead of a screenshot.

Methodology

Benchmark figures are from 48 AskedAbout free checks of real U.S. dental practices (19 cosmetic, 24 general, 5 family) in 18 metros, run between June 27 and September 9, 2026, counted again from the production audit records on October 2, 2026 with the repository's canonical cut (ops/audit_cut.py: synthetic and smoke rows excluded by business name, probe addresses excluded). Each free check submits three patient-style questions about the practice's own city to two answer engines, ChatGPT and Perplexity, for six answers per practice; 280 answers completed. A judge model marks each completed answer for whether the practice asked about is named and which competitors are named; the AI Visibility Score (0 to 100) is computed over completed answers. "Named in 39.3% of answers" is 110 of 280 completed answers; the per-engine and per-type rows are the same flag split by engine and by the vertical the check was run under. "Out-named by one rival" means a single competitor named more often than the practice asked about, 23 of 48. "193 distinct rival practices" is the count of unique competitor names across the 48 audits (474 mentions). The paid $79 Full Audit widens this to 25 questions across four engines (adds Gemini and Claude), sampled three times each (300 scheduled answers). We query official engine APIs, which approximate but do not perfectly match the consumer apps. Subjects anonymized except where a competitor is named as a public business in a public answer; aggregate benchmark data only, never patient-identifiable; figures refresh as the audit corpus grows.

See also

• Built for your practice: AI visibility for dentists — the vertical page with the fix checklist.
• The category and the other engines: why AI recommends competitors instead of you · how to show up in ChatGPT recommendations · how to get recommended by Perplexity.
• The data: the local-business AI visibility benchmark · AI answers change every time you ask · AI visibility tools compared.
• Other local verticals: why AI ignores your med spa · why AI ignores your law firm · why AI ignores your home-services company.

FAQ