Not in one uniform way. In Citevio's June 2026 scan, Google star ratings did not distinguish practices named from those not named by ChatGPT or Perplexity. Review count showed no measurable relationship on ChatGPT, where only three practices were named, but was higher among practices Perplexity named. That is an observed association in a small, uneven sample—not proof that reviews cause AI recommendations.
Review advice for dental practices is usually written as if the relationship were settled: collect more reviews, hold a high rating, and the assistants will start naming you. We tested that on our own scan records instead of repeating it. This page reports what the review data did and did not separate, on each engine, with the sample sizes in view.
What did the scan find about reviews and AI naming?
Star rating separated nothing on either engine. Review count separated nothing on ChatGPT, where only 3 practices were named, but named practices had a higher review-count median on Perplexity.
“Named” means a practice had a VISIBLE result in the engine field of the scan record. Each row is one deduplicated dental-practice website, not necessarily one independent clinic location. We compared the Google rating and review count in that row with named versus not-named status.
| Engine and measure | Valid review + engine rows | Named practices | Named median | Not-named median | Association result |
|---|---|---|---|---|---|
| ChatGPT review count | 411 | 3 | 296 | 263.5 | AUC 0.594; two-sided p=0.576; no measurable separation |
| ChatGPT star rating | 411 | 3 | 4.9 | 4.9 | AUC 0.442; two-sided p=0.723; no measurable separation |
| Perplexity review count | 297 | 18 | 578 | 239 | AUC 0.704; two-sided p=0.0037; positive exploratory association |
| Perplexity star rating | 297 | 18 | 4.9 | 4.9 | AUC 0.586; two-sided p=0.199; no measurable separation |
The star-rating result is the consistent part: the two groups had the same 4.9 median on both engines. The review-count result is not uniform. With only three ChatGPT names, the scan cannot establish a useful ChatGPT relationship. In Perplexity, named practices had a higher review-count median.
Does the Perplexity result mean that collecting reviews earns a recommendation?
No. The Perplexity finding is an association in a cross-sectional scan, not a before-and-after test of collecting reviews.
The result remained in the subset with stored, query-level evidence that agreed with the overall status: 15 named practices among 265 valid rows, with review-count medians of 573 versus 239, AUC 0.730 and two-sided p=0.0027. That sensitivity check makes the signal worth reporting, but it is not an independent replication and does not show why the practices were named.
City, specialty, brand recognition, website information, query intent and other unmeasured factors may relate both to review count and to an engine's answer. The scan did not assign practices to collect reviews or hold those factors constant. A practice should not treat this result as a forecast that a review campaign will change Perplexity or ChatGPT.
Did star ratings separate practices named by either engine?
No. In this scan, the named and not-named groups both had a 4.9 median Google star rating for ChatGPT and for Perplexity.
This says only that star rating did not separate the groups here. It does not mean that service quality, accurate profiles or genuine patient feedback do not matter. Google says review count and positive ratings can help local ranking, but Google local ranking and an AI answer are different outcomes. This analysis does not establish that improving a rating changes an AI recommendation.
Do the common 50-review and 4.7-star claims hold up in this scan?
No. Tests of the two published claims did not support either as a verified rule for ChatGPT or Perplexity in this sample.
We tested “50 or more reviews,” “4.7 stars or more,” and the two together as binary group comparisons. Fisher's exact tests did not support any of the three claims: ChatGPT p=1.000 for each comparison; Perplexity p=0.142, p=0.490 and p=0.085 respectively. Those figures are tests of claims that already circulate online, not targets we recommend.
| Engine | Claim tested | Named among practices meeting the claim | Named among other practices | Fisher two-sided p | Reading |
|---|---|---|---|---|---|
| ChatGPT | 50 or more reviews | 3/354 | 0/57 | 1.000 | Not supported in this sample |
| ChatGPT | 4.7 stars or more | 3/350 | 0/61 | 1.000 | Not supported in this sample |
| Perplexity | 50 or more reviews | 18/260 | 0/37 | 0.142 | Not supported in this sample |
| Perplexity | 4.7 stars or more | 17/253 | 1/44 | 0.490 | Not supported in this sample |
Most practices meeting either claim were still not named. The tests also cannot prove that the values have no effect; they show that these two sharp claims were not supported as engine rules in this dataset.
Where can you compare your practice's review count?
Use the separate dental practice Google review benchmark for the June 2026 distribution, percentiles and measurement method. This page answers a different question: whether review data separated named from not-named practices.
Keeping the questions separate matters. A benchmark describes what was observed in a sample. It does not tell a practice what number will produce an AI recommendation.
Why are the 523 review profiles and 64 answers not one dataset?
They measure different things and must not be combined. The 523 profiles are June 2026 Google review readings for the market scan; the 64 answers are Citevio's August 2026 agency-selection measurement.
The market scan produced 523 valid Google rating and review-count profiles after production de-duplication. The 64-answer set is 16 agency-selection questions across two engines and two runs, used to measure Citevio's own visibility. It is not a clinic-level answer set, so it is not in the numerator, denominator or correlation on this page.
What are the limits of this analysis?
The positive groups are small, engine coverage is uneven, and the scan is observational. The result is useful for rejecting overconfident claims, not for proving a review tactic.
- Small positive groups. ChatGPT had only 3 named practices in 411 valid review-and-engine rows.
- Uneven engine coverage. Perplexity had 18 named practices in 297 rows; only 15 named cases were in the query-level evidence subset.
- Systematic API errors in the raw scan. 112 ChatGPT
API_ERROR_429records and 230 PerplexityAPI_ERROR_401records among the 527 included sites. - Partial evidence retention. Historical Perplexity records do not retain answer bodies, and only part of the historical scan has query-level evidence.
- No repeated runs. The records have no repeated-run field, so model variation was not measured.
For the wider diagnostic process, see why a dental practice may not appear in ChatGPT, use the manual ChatGPT visibility check, and read how Citevio measures AI visibility. You can also inspect the open dental market research data.
Should a practice buy Google reviews to improve AI visibility?
No. This analysis does not provide a reason to buy or manipulate reviews, and Google and FTC rules address fake reviews and incentives conditioned on a particular sentiment.
Google's user-generated content policy and the FTC's Consumer Reviews and Testimonials Rule Q&A set the relevant rules. Ask for genuine feedback through a neutral process. This page is not legal advice.
Reviews are one of several signals an engine can read about a practice. The checker below reads the ones on 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
We ran the production de-duplication and dental-practice inclusion rules over antigravity/teshis_*.json, then used the Google Business Profile review fields and each engine's recorded VISIBLE or NOT VISIBLE status. The unit is a deduplicated website. The E.1 analysis was rerun on 14 August 2026 with the read-only script antigravity/e1_review_visibility_analysis_sol_20260814.py.
The scan included 527 dental-practice websites after filtering; 523 had valid rating and review-count readings. AUC here is the probability that a named row has a higher value than a not-named row, with ties shared. The p-values are two-sided Mann–Whitney tests for the continuous comparisons and Fisher tests for the published binary claims. They are exploratory and are not corrections for multiple testing.
Sources: Citevio's 14 August 2026 E.1 read-only analysis of June 2026 raw scan records; Citevio open dental market research; Google Business Profile Help; Google Maps user-generated content policy; FTC Consumer Reviews and Testimonials Rule Q&A.
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
Do Google reviews affect ChatGPT recommendations for dentists?
The ChatGPT part of this scan found no measurable review-count or star-rating separation, but only 3 of 411 valid rows were named. That small positive group cannot support a causal claim or a strong negative conclusion.
Do Google reviews affect Perplexity recommendations for dentists?
Named practices had higher review counts in the Perplexity rows: a 578 median versus 239, AUC 0.704 and two-sided p=0.0037. That is exploratory association, not evidence that gaining reviews causes Perplexity to name a practice.
What Google star rating does a practice need for ChatGPT?
This analysis found no verified rating requirement. The named and not-named groups both had a 4.9 median rating in the ChatGPT rows, and the scan did not test a rating intervention.
How many Google reviews does a dental practice need?
This page does not set a target. See the Google review benchmark for dental practices for the sample distribution; a distribution is not a promise about an AI answer.
Does buying Google reviews help AI visibility?
Do not buy or manipulate reviews. The study does not show that it changes an AI answer, while Google policy and FTC rules create a separate compliance risk.