What AI search says about aesthetic practices

Who this is for

This is for the physician or medical professional who owns an aesthetic practice, and for the operations lead who manages the practice's listings and reviews. Both can act on it.

Patients now ask AI engines which practice to choose in their city, and the engines answer by naming a few practices.

In July 2026 we read about 2,800 answers from Claude, ChatGPT, Gemini and Perplexity about real practices in 18 markets in the United States, Canada and Mexico. For every answer we recorded two things: whether the practice was named in the recommendation, and whether the practice's own website was used as a source. Practice names are withheld.¹

Do patients use AI to choose an aesthetic practice?

The highest spenders do.

Bain & Company, working with Comite Colbert, surveyed luxury buyers in April 2026. Fifty-four percent of United States luxury buyers used AI during their most recent luxury purchase. The number rises with spending: 82% of the heaviest spenders used AI, compared with 28% of the lightest.²

A 2025 survey by rater8, a reputation-management vendor, found 70% of United States adults are open to using or already use AI to research physicians. It is a vendor survey.³

For a practice, the heaviest spenders are the patients most likely to ask an AI first.

Does market size change how often AI names a practice?

In our measurements, the smaller the market, the more often the practice got named.¹

Practices in the largest, densest markets were named in a small share of answers: 8% in Manhattan, 7% in Dallas, 10% in Miami, 2% in Mexico City, 2% in Las Vegas.¹

Practices in mid-size and suburban markets were named far more often: 60% in the Twin Cities, 55% on the Mexican border, 50% in Boise, 46% in an LA suburb, 46% in a Vancouver suburb, 35% in London Ontario.¹

In a small market the effect is stronger still. In Erie, Pennsylvania, two practices were each named in more than half of all answers, and the top four practices absorbed nearly every recommendation.¹

The pattern held in the United States, Canada, and Mexico.

For a practice outside a top-ten metro, being named by AI in its market is within reach now, and in some markets a few practices are already named in most answers.

Why does AI read my website and then recommend someone else?

In one Chicago-area market the practice's website was used as a source in 47% of answers while the practice was named in 22%. In Pittsburgh, 38% versus 22%. In Las Vegas, 17% versus 2%.¹

When an engine answers a question like this, it runs live searches, pulls somewhere between five and fifteen pages (review platforms, directories, and several practices' own websites), reads them, and then features a few practices by name.¹

So a practice with strong content makes the shortlist. Its pages are retrieved and read. It loses the feature slot at the last step, when the engine compares the candidates.

The engines say what they compare on. They write it into the answer. One verbatim example from our transcripts, justifying a pick: "strong recent review volume (about 4.8/5 from 200+ reviews)."¹

Across the answers, the sources engines lean on for those comparisons are review platforms and directories: Yelp appeared 733 times, RealSelf 595, Healthgrades 188.¹

Bain found the same pattern for brands. Its 2026 analysis of AI citations found 89% of answers to prompts that do not name a brand were fulfilled by third-party sources, with little variation by AI platform.⁴

Being read as a source and being named were separate results, and the reason the engines gave for naming a practice was its reviews.

When we asked the engines what a practice should be doing, their advice drew on the same ten areas every time. The first five: website fundamentals (structure, speed, mobile experience, real service pages); local presence (Google Business Profile, map pack visibility, consistent listing information); reviews and reputation (volume, rating, and whether the practice responds); paid advertising (whether the practice shows up in paid results at all); and social media (cadence, engagement, and whether posts lead anywhere).¹

The other five: email and text follow-up; content and patient education; positioning (whether the practice explains what makes it different); booking friction (how many steps from interest to appointment); and measurement (whether anyone can tell which of the other nine is working).

Two of these carry unusual weight in AI answers specifically. Reviews decide the feature slot. Google Business Profile decides whether the practice appears in the local layer the engines lean on.

When a practice owner asks an AI how to get more patients, the engines cite practice-management software vendors and industry associations more often than they cite marketing agencies. Those companies win by publishing plain, structured, evergreen explanations of how things work. YouTube and Reddit were also among the most cited sources across those answers.¹

The advice we pulled was generic. No answer cited a measurement of a specific practice in a specific market.¹

Does one AI engine show where a practice stands?

No. The same practice, asked the same questions on the same day, scored very differently by engine.¹

One Los Angeles practice was named in 60% of Claude's answers and 20% of ChatGPT's. A Chicago practice: 26% and 13%. A Minneapolis practice: 68% and 46%.¹

Any report built on a single engine will tell a practice that it is winning or losing, and it has a good chance of being wrong. Answers also vary between runs of the same question.

A practice can check its own position by asking the question a patient would ask, for its city and procedure, several times and on at least two engines, and noting separately whether it was named and whether its website was listed as a source.

Named: hold it, and watch it, because these answers change.

Cited but never named: the content is working and the comparison signals are not, and reviews are the first place to look.

Neither: the Google Business Profile and the review base come first, because that is the layer the engines read first.

About this review

We read about 2,800 answers in July 2026, across 18 markets in the United States, Canada and Mexico. All four engines (Claude, ChatGPT, Gemini and Perplexity) were measured in 7 markets, and Gemini and Perplexity in the other 11, so the engine-comparison numbers come from the seven full runs. Patient-use data points come from Bain & Company with Comite Colbert, an industry survey, and from rater8, a vendor. The brand-level comparison comes from Bain's 2026 analysis of AI citations. We run this measurement as part of FAx, our audit of a practice's public presence.

Markets are named by region because the practices behind them are published de-identified and a town name would undo that. Most markets tracked a single practice, with no full competitive set. AI answers change over time, so these are a July 2026 snapshot. And the relationship between reviews and being featured comes from what the engines say they are doing; no controlled experiment has tested it.

Fractional Aesthetics is a growth strategy practice for aesthetic medical businesses.