In our June 2026 scan of 527 US dental practices, the median practice had 250 Google reviews at a 4.9-star average, and 96.7% already sit above 4.3 stars, the level ChatGPT-recommended locations averaged in SOCi's 2026 index. For most practices, review count and rating are close to solved. If your listing still isn't showing up in AI answers, review volume is unlikely to be why.
"How many reviews do I need" is usually the wrong question, because almost every practice in our sample already sits above the star average of the locations AI assistants are reported to recommend. The more useful question is where your own numbers sit against practices like yours, and whether reviews are actually the thing holding your visibility back or just the thing that's easiest to worry about. This page gives you the benchmark; further down, it also tells you honestly what the benchmark does not prove.
How many Google reviews does the typical practice have?
The median practice we scanned has 250 Google reviews at a 4.9-star average. A quarter of practices have fewer than 102 reviews; a quarter have more than 474, and the busiest tenth have topped 764.
| Percentile | Google reviews | Star rating |
|---|---|---|
| Fewest (min) | 1 | 2.9 |
| 25th percentile | 102 | 4.8 |
| Median | 250 | 4.9 |
| 75th percentile | 474 | 5.0 |
| 90th percentile | 764 | 5.0 |
| Most (max) | 2,972 | 5.0 |
Two things are worth separating here. Review count spreads out a lot: a practice at the 90th percentile has more than 7 times the reviews of one at the 25th. Star rating barely spreads at all: three quarters of practices sit at 4.8 stars or higher. That gap matters for what comes next, because rating is close to a ceiling effect across this sample, so it's a weak tool for telling practices apart, no matter which AI engine is doing the sorting.
Source: Citevio dental practice web anatomy study, June 2026, n = 523 of 527 practices scanned in 7 US metros plus 9 smaller nearby towns. Full dataset and CSV: citevio.com/data.
The review-distribution tables point to a fixed archive record with DOI 10.5281/zenodo.22016876.
Is my star rating good enough for AI to notice me?
Probably. In SOCi's 2026 local visibility index, the locations each engine actually recommended averaged 4.3 stars on ChatGPT, 4.1 on Perplexity and 3.9 on Gemini. In our scan, 96.7% of practices sit at or above 4.3 stars and 98.7% at or above 4.1, so most practices reading this are very likely already inside every one of those ranges.
| Engine | Avg rating of the locations it recommended | Practices at or above that average | Share of 523 |
|---|---|---|---|
| ChatGPT | 4.3★ | 506 | 96.7% |
| Perplexity | 4.1★ | 516 | 98.7% |
| Gemini | 3.9★ | 522 | 99.8% |
Google's own guidance on local ranking says plainly that "more reviews and positive ratings can help your business's local ranking," without spelling out an exact number or cutoff. That fits what we found: a soft, saturated signal rather than a hard gate. If your practice is anywhere near the median in the table above, star rating is not the lever to pull next.
Sources: SOCi 2026 Local Visibility Index, reported by Search Engine Land, January 2026; Google Business Profile Help, "Improve your local ranking on Google", checked August 2026.
If my reviews are fine, why doesn't ChatGPT recommend my practice?
Reviews are close to a solved problem across almost the whole sample, so they don't look like what's holding most practices back. Across the same 527-practice scan, the pattern that stood out among the practices that were not named was machine-readable schema: 53.9% had no LocalBusiness or Dentist schema on their site at all. We did not test whether adding it changes the outcome.
We cover that finding in full, including why schema looks like foundational infrastructure rather than a guaranteed lever, on why AI recommends some dentists and not others. This page exists to answer a narrower question, what's a normal review count and rating, and that page answers a different one, what do the rarely-named practices have in common. Read together, the short version is: your reviews are probably fine, and the more useful thing to check next is what an engine can actually read off your site.
What this data does not show
We are not saying review scores don't affect whether ChatGPT or Perplexity names a practice. We have not tested that claim, and we're not going to imply we did. Almost every practice we scanned already clears the star thresholds engines are said to use, so review scores alone don't explain why one gets named and another doesn't.
Testing whether reviews change an AI recommendation would mean splitting the practices each engine actually named into rating and review-count groups and comparing them against the practices it didn't name. Our June 2026 scan only returned a usable ChatGPT reading that named 3 practices and a Perplexity reading that named 18, and nearly all of them already sit in the same top-rated band as everyone else. Dividing 3 or 18 names into buckets doesn't produce a comparison, it produces noise dressed up as a finding, so we chose not to publish one. If a future scan returns enough named practices to test this properly, we will publish that comparison and link it from this page.
Something we can measure and separately have not is what happens after a rating is already good: whether more reviews past a certain point keeps helping, or whether the return flattens out. Our data has the count, not the causal test, so we describe the shape of the distribution above and stop there.
How do practices with hundreds of reviews actually get them?
By asking every patient, the same way, every time. The practices sitting well above the median did not run a campaign; they built a habit into the visit. That is also the only route Google's policy allows — the invitation has to go out without selecting who receives it, without a condition attached, and without pressure applied while the patient is still in the building.
The distribution on this page is worth reading as context rather than as a target. Half the practices scanned had more than 250 reviews and half had fewer; the median rating was 4.9, and the median review ran about 77 words. Those are descriptions of a sample of 523 practices, not thresholds an engine is known to apply — and this page's own data is the reason to be careful with them: almost everyone already clears the star levels the industry talks about, and it did not make them visible.
Source: Citevio dental practice web anatomy study, 6–26 June 2026. Review figures n = 523 of 527 practices; four had no rating data on file.
What a compliant process looks like is short enough to write in five lines, and the constraints come from the policy rather than from us.
- Everyone gets asked. No pre-screening question deciding who receives the invitation. Filtering by predicted sentiment is what Google calls selectively soliciting positive reviews.
- Nothing is offered in exchange. No discount, no whitening voucher, no entry into a draw — for a review, for changing one, or for taking one down.
- Not at the front desk. Google states directly that merchants should not require or pressure users to leave reviews while on the premises. Send it afterwards.
- One consistent moment. Pick the point in the visit where the request happens and keep it the same, so the habit survives a busy week.
- Unhappy patients get a real reply, not a diversion. Handling a complaint properly is good practice; routing it away from the public form to protect an average is the thing the policy names.
Policy source: Google Maps user-generated content policy, read 18 August 2026. This is a reading of a platform policy, not legal advice.
Three things people ask about at this point have not been measured by us, and we would rather name them than imply an answer. Whether review velocity or recency matters was not tracked in this pass — only the count and average on file at scan time. Whether replying to reviews changes anything was not measured. Whether review-request software outperforms a member of staff sending the same message was not measured either. Each of those can be argued as sensible process; none of them can be presented here as a performance finding.
And the question a page on an agency's website is expected to answer: does an agency run this for you? Where a plan includes it, the request process is set up and monitored under the same rules above — no screening, no incentives — and what that covers is described on the Citation-to-Chair Protocol page rather than here, because this section is about what any practice should do whether or not it hires anyone. Whether any of it feeds an AI recommendation is treated separately, and sceptically, in do Google reviews affect ChatGPT recommendations and why AI recommends some dentists.
Reviews are one part of what an AI engine sees about a practice, alongside things we did measure directly, like whether a site's robots.txt blocks AI crawlers outright. The checker below reads several of these signals for your own site in under two minutes.
Prefer to question the engines by hand? See how to check what ChatGPT says about your practice.
How we measured this
The sample is 527 US dental practice websites in Charlotte, Austin, Raleigh, Nashville, Columbus, Tampa and Salt Lake City, plus 21 practices in 9 smaller nearby towns, scanned between 6 and 26 June 2026, the same set behind our other web-anatomy studies. For each site we pulled the current rating, review count and photo count from its Google Business Profile. 523 of 527 practices had a rating on file; the remaining 4 did not, so they are simply absent from the numbers on this page rather than counted as a zero or a failure. Where a practice was scanned more than once in the window, the most recent reading is used, and if that reading was itself missing a field, the most recent valid reading for that specific field is used instead.
- A snapshot, not a trend. Every number here carries the June 2026 scan window; ratings and counts move over time, especially for smaller practices where a handful of new reviews can shift the number visibly.
- One reading per practice. We did not track review velocity or recency in this pass, only the count and average on file at scan time.
- Sample, not census. This is 7 metros plus 9 nearby towns, not a national sample. We publish a national figure only once we can read at least 2,000 sites; this study does not meet that bar.
- Engine figures are external reference points, not our own test. The 4.3 / 4.1 / 3.9 star averages above come from SOCi's 2026 Local Visibility Index already used elsewhere on citevio.com. We did not independently verify how precisely, or whether, each engine enforces them.
The full framework behind our scans, scores and city reports is in how we measure AI visibility, and the underlying dataset is free to download at citevio.com/data. If you think a number on this page is wrong, email contact@citevio.com with the domain and what you saw. We would rather be corrected than quoted wrongly.
Citevio's dental market research is published openly under CC-BY-4.0 with a DOI, so the numbers can be checked and reused; client data is never published.
Common questions
How many Google reviews does a dental practice need?
In our June 2026 scan of 527 US dental practices, the median practice had 250 Google reviews at a 4.9-star average, based on the 523 practices with a rating on file. A quarter had fewer than 102 reviews, and a quarter had more than 474. There is no single number that counts as "enough," but 250 reviews at a high-4-star average is the typical practice, not an exceptional one.
What star rating do ChatGPT, Perplexity, Gemini and Google AI Overviews favor?
In SOCi's 2026 local visibility index, the locations each engine actually recommended averaged 4.3 stars on ChatGPT, 4.1 on Perplexity and 3.9 on Gemini. Treat these as averages of what got recommended, not confirmed cutoffs each engine enforces exactly. In our scan, 96.7% of practices with a rating on file already clear 4.3 stars, 98.7% clear 4.1, and 99.8% clear 3.9.
Does buying reviews help my AI visibility?
No, and it is not worth the legal risk. Google's review policies prohibit paying for reviews, offering incentives or filtering out unhappy patients, and the FTC's rule against fake and paid reviews carries penalties that can reach $53,088 per violation. It is also unnecessary for most practices: 96.7% of the ones we scanned already sit above the 4.3-star average of ChatGPT's recommended locations, so a normal, unfiltered review process is generally enough to clear it.
If my star rating and review count are already good, why isn't my practice recommended by ChatGPT?
Reviews are close to a solved problem across almost the whole sample we scanned, so they don't look like what's holding most practices back. Across the same 527-practice scan, the pattern that stood out among the practices that were not named was machine-readable schema: 53.9% had no LocalBusiness or Dentist schema at all. We did not test whether adding it changes the outcome. We cover that finding on a separate page.
How many reviews does it take to be in the top 10% of dental practices?
In our June 2026 scan, the 90th percentile was 764 Google reviews. A quarter of practices had more than 474, so the distance between a typical practice and the busiest tenth is spread across a fairly wide range, not one sharp jump.
What's the smallest and largest number of Google reviews you measured?
The smallest was 1 review at 2.9 stars; the largest was 2,972 reviews at 5.0 stars, among the 523 of 527 US dental practices we scanned in June 2026. Both are single practices at the edge of the range, not typical ones.
Does having more reviews also mean a higher star rating?
Not really. Review count spreads out a lot — a practice at the 90th percentile has more than 7 times the reviews of one at the 25th — but star rating barely moves: three quarters of the practices we scanned sit at 4.8 stars or higher. Rating is close to a ceiling across this sample, so it doesn't separate busy and quiet practices the way review count does.
Are the 4.3, 4.1 and 3.9 star figures a test Citevio ran itself?
No. Those come from SOCi's 2026 Local Visibility Index, an average across roughly 350,000 locations in all business categories, not dental-specific, and we did not independently verify how precisely, or whether, each engine enforces them. They're reference averages, not a threshold any engine is confirmed to require.
Does Google publish an official minimum number of reviews?
No. Google's own guidance says "more reviews and positive ratings can help your business's local ranking," without naming an exact number or cutoff. That matches what we found: a soft, saturated signal rather than a hard gate most practices are failing to clear.
How long is a typical Google review for a dental practice?
About 77 words, based on the median review length in our June 2026 scan of 523 practices. That's a description of the sample, not a target — nothing in our data says a longer review carries more weight.
Is it against the rules to only ask my happiest patients for a Google review?
Yes. Google's policy lists selectively soliciting positive reviews in the same category as paying for reviews. A pre-screening question that filters out patients you think might leave a lower rating is that practice, whatever the review software calls it.
Can I offer a discount or gift for a Google review?
No. Nothing can be offered in exchange for a review — no discount, no whitening voucher, no entry into a draw — for posting one, changing one, or taking one down. Citevio's own review process follows the same rule and never offers incentives.
Should staff ask for a review while the patient is still in the office?
No. Google states directly that merchants should not require or pressure patients to leave a review while they're still on the premises. Send the request afterward, at one consistent point in the follow-up.
Is this review benchmark a national number, or just a few cities?
Just a sample. It covers 527 practices in Charlotte, Austin, Raleigh, Nashville, Columbus, Tampa and Salt Lake City, plus 21 practices in 9 smaller nearby towns, scanned in June 2026. We publish a national figure only once we can read at least 2,000 sites, and this study doesn't meet that bar.
Does this benchmark track how fast a practice is gaining new reviews?
No. We recorded the review count and star rating on file at scan time, not the rate at which reviews arrived. Review velocity and recency were not tracked in this pass.