When New York patients ask AI for a dentist, who gets named?
We put 100 real patient questions — "best dentist in New York", "emergency dentist", "Invisalign cost", "solo practice vs a chain" — to ChatGPT and Gemini and Perplexity and checked which of 21 identifiable New York City dental practices each answer named. 9 of the 100 questions got no named practice at all — AI answered them with directories, review sites, or nobody. Run the check for your practice →
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Why we ran this
Choosing a dentist is now something people ask an AI assistant, not just Google. "Best dentist in New York", "dentist near me accepting new patients", "is a chain clinic as good as a private practice" — the assistant reads a handful of sources and either names specific practices or it doesn't. This benchmark reads how visible New York dental practices currently are, engine by engine, and where the openings are — including whether solo practices, group clinics and specialist centres are treated differently.
How we measured it
We wrote 100 questions in the language a patient actually uses, across six clusters (finding a dentist; solo vs group vs corporate practice; implants & oral surgery; orthodontics; cost & insurance; emergencies & booking — all 100 are explorable in full below). Each question was sampled once per engine, and every answer was checked for a mention of any of 21 identifiable New York practices — multi-specialty groups like 209 NYC Dental and Herald Square Dental, cosmetic and orthodontic specialists like Rosenthal Apa Group and Central Park Orthodontics, and individually-branded practices — using the same name matcher a live Inserviss scan uses. The tracked list is grounded in web research, not guesswork.
A single pass per engine establishes direction and priority; it is not a trend line. Citations are reported as measured; query volumes are labelled estimates, shown as ranges. Engines are never blended. This run covers ChatGPT and Gemini and Perplexity; Microsoft Copilot is not currently sampled.
The tracked list covers the 21 practices a patient would plausibly consider, whether or not any engine named them. Five were added after the first run — Tend, Aspen Dental, ClearChoice, Great Expressions and SmileDirectClub — and re-matched against the same stored answers, with no new sampling, so every figure here rests on identical evidence.
ChatGPT hit its API quota partway through this run and returned an answer for 65 of the 100 questions; every ChatGPT figure here is computed on those 65 answers only — never scaled up to look like a full run. Gemini and Perplexity completed all 100. A re-run closes the gap; the measurement itself is unchanged.
What we found, at a glance
- Visibility varies sharply by engine. ChatGPT named a tracked practice in 15 of 65 answers, Gemini named a tracked practice in 87 of 100 answers and Perplexity named a tracked practice in 48 of 100 answers.
- 9 of 100 questions got no tracked practice on any engine — the demand goes to directories and review aggregators instead. Weakest: Orthodontics — braces & Invisalign.
- NYU College of Dentistry is the most-named practice, appearing in 76 of 300 answers. Only 17 of the 21 tracked practices were named even once.
- Practice type matters less than you’d think. Whether a name is a solo practice, a group clinic or a specialist centre, the table below shows how thin the coverage is across all three.
How often does AI name a practice at all?
Below is the share of each engine's answers that named at least one tracked practice — then the full table, every practice against every engine, sortable by any column.
Share of each engine's answers naming at least one of the 21 tracked practices. Per engine, never blended.
| Firm | ChatGPT | Gemini | Perplexity |
|---|---|---|---|
| NYU College of Dentistry | 5 | 58 | 13 |
| 209 NYC Dental | 4 | 34 | 19 |
| Columbia University College of Dental Medicine | 2 | 47 | 6 |
| Tend | 1 | 32 | 16 |
| Dental365 | 0 | 16 | 5 |
| Central Park Orthodonticsspecialist | 1 | 6 | 5 |
| Manhattan Dental Studio | 1 | 6 | 4 |
| Central Park West Orthodonticsspecialist | 0 | 5 | 4 |
| ClearChoicespecialist | 2 | 7 | 0 |
| DDS Group | 1 | 2 | 3 |
| Apa Aestheticspecialist | 0 | 5 | 0 |
| NYC Smile Designspecialist | 1 | 2 | 0 |
| Aspen Dental | 0 | 3 | 0 |
| Herald Square Dental | 0 | 2 | 0 |
| Smile Central Park | 1 | 0 | 1 |
| Rosenthal Apa Groupspecialist | 0 | 1 | 0 |
| New York Center for Cosmetic Dentistryspecialist | 0 | 1 | 0 |
| Gallery57 Dental | 0 | 0 | 0 |
| Dr. Marc Lazare | 0 | 0 | 0 |
| Great Expressions Dental Centers | 0 | 0 | 0 |
| SmileDirectClubspecialist | 0 | 0 | 0 |
Mentions across 100 answers per engine. Select a column heading to re-sort. Scroll the table sideways on a narrow screen.
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Where the openings are
Coverage varies by question type. Below is the best engine’s coverage for each cluster, out of ~17 questions.
Answers naming a practice on the strongest engine, per cluster (out of ~17).
How this is estimated
Estimate — a range, not a keyword-tool export. Illustrative estimate, not a keyword-tool export. New York City is a large local market; each cluster has ~16-17 seed questions, long-tail intent phrases run ~40–220 monthly searches each across the NYC area, one or two head terms add ~900–4,500/mo, and an AI-assistant reformulation multiplier of ×2–4 accounts for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific practice, the real number comes from the check, not this table.
| Cluster | Best-engine coverage | Est. questions / mo |
|---|---|---|
| Finding & choosing a dentist | 15/17 | 6,000–22,000 |
| Solo vs group vs corporate practice | 15/17 | 2,600–10,000 |
| Implants & oral surgery | 15/17 | 4,500–17,000 |
| ●Orthodontics — braces & Invisalign | 11/17 | 4,200–16,000 |
| Cost, insurance & payment | 15/16 | 3,800–14,500 |
| Emergencies, booking & access | 16/16 | 5,000–19,000 |
Volumes are an estimate, shown as a range. Illustrative estimate, not a keyword-tool export. New York City is a large local market; each cluster has ~16-17 seed questions, long-tail intent phrases run ~40–220 monthly searches each across the NYC area, one or two head terms add ~900–4,500/mo, and an AI-assistant reformulation multiplier of ×2–4 accounts for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific practice, the real number comes from the check, not this table.
The check runs this same 100-question universe against your practice’s name, per engine, and returns your AI Visibility Score plus the exact questions where a competitor — or no one — is being named.
Check your practice’s AI visibility →Explore the 100 questions
Every question, and exactly what each engine did with it — which practices it named, and which sources it pulled from. Filter by cluster, search for your own name, or show only the questions no practice has claimed.
The 9 questions no tracked practice owns on any engine, with the sources AI cites instead — a ready-made content brief.
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Where each engine gets its answers
The domains each engine actually pulled from — never blended. The mix differs sharply by engine: some answers are built almost entirely from directories and review aggregators, others from individual New York practice websites and Reddit threads. Where a tracked practice's own site appears, it's marked in blue below.
Times a domain was cited across the 100 answers. Blue = a tracked practice's own site.
About this study
- Single pass per engine. One sample per question per engine establishes direction; it does not average out run-to-run answer variance.
- Snapshot in time. Collected September 10, 2026. AI engines change their answers week to week; this is not a trend line.
- Identical question set to the Zurich study. The two location-dedicated dental studies use the same 100 questions with the city swapped, so Zurich and New York are directly comparable.
- ChatGPT and Gemini and Perplexity this run. Microsoft Copilot is not currently sampled; the measurement is identical when it is.
How to cite this study
To request the underlying data or discuss a practice-specific analysis, contact igor@inserviss.app.
The same measurement, run against your name — per engine, question by question — with your score and the questions to claim first.
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