AI Clinic Recommendation Statistics (2026): 12 Findings From 8,385 AI Answers
The most-quotable statistics from the KailxLabs study of which GLP-1 and medical weight loss clinics ChatGPT, Perplexity, Gemini, and Google AI actually recommend. Free to cite, CC BY 4.0.
The numbers. Twelve statistics on who AI recommends when patients ask for a clinic, drawn from 8,385 real answers across five engines and 75 US metros. Every figure is free to cite under CC BY 4.0. Source: KailxLabs, https://www.kailxlabs.co/research/who-ai-recommends-glp1-referral-economy-2026.
Short answer. AI clinic recommendations are unstable: the exact named set changes 96% of the time across repeat runs, and even the top clinic changes 68% of the time. The engines barely agree with each other, 12.6% on average. The biggest lever a clinic controls is its own website, 57.8% of visible cited sources are provider or other websites, alongside its structured entity graph, 2.17x citation odds before adjustment and 1.45x after. These are associations, not proven causes. The full methodology and open dataset are in the complete study.
These are the headline statistics from Who AI Recommends: The GLP-1 Clinic AI Referral Economy (2026), an AI visibility benchmark. Each is written to be quoted directly. If you cite one, attribute KailxLabs and link to this page or the full study. Being named by AI is a marketing-visibility signal, not a measure of clinical quality.
How consistent are AI clinic recommendations across repeat searches?
AI clinic recommendations are non-deterministic: ask the same engine the same question twice and the exact set of clinics it names changes 96% of the time, and even the single top clinic changes 68% of the time (KailxLabs, 2026, 8,385 answers).
Where do AI engines get the clinic names they recommend?
57.8% of the visible sources AI cites for a clinic recommendation are provider or other websites, including the clinic’s own, far ahead of Google surfaces at 37.4% and any single directory (KailxLabs, 2026).
How often do different AI engines agree on which clinic to recommend?
AI engines agree on which clinic to recommend only 12.6% of the time on average, ranging from 7% to 19% by engine pair, so which clinic a patient hears depends heavily on which assistant they use (KailxLabs, 2026).
What on-site signal is most associated with a clinic being named by AI?
A connected entity graph, meaning Schema.org sameAs links tying a clinic to its own profiles, is the on-site signal most associated with being named by AI, at 2.17 times higher odds before adjustment and 1.45 times after controlling for the clinic’s Google strength (KailxLabs, 2026).
Do Google AI Overview and Google AI Mode recommend the same clinics?
Google AI Overview and Google AI Mode agree on which clinics to recommend only 17.4% of the time, even though both are Google’s own products (KailxLabs, 2026).
Which AI engine relies most on provider websites?
ChatGPT drew 97.6% of its visible clinic citations from provider or other websites rather than directories, the highest share of any engine measured (KailxLabs, 2026).
Does saying Ozempic instead of semaglutide change which clinics AI names?
The clinics AI names for a generic drug term and for the brand term overlap only 28.5%, so the same active ingredient asked different ways surfaces largely different clinics (KailxLabs, 2026).
How much of AI clinic recommendation share goes to national brands?
Brands cited across five or more metros captured 10.1% of all AI clinic recommendations, mostly multi-location chains with local pages, while pure telehealth brands were only 1.8% (KailxLabs, 2026).
Can a clinic rank well on Google and still be invisible in AI search?
16.3% of clinics that are strong on Google, in the local pack or top five, are never named by any AI engine, because AI does not simply re-rank Google (KailxLabs, 2026).
How large was the AI clinic recommendation study?
The study captured 8,385 real AI answers across five engines (ChatGPT, Perplexity, Gemini, Google AI Overview, Google AI Mode) and 75 US metros in May 2026 (KailxLabs, 2026).
Does Schema.org structured data help a clinic get cited by AI?
A Schema.org @graph was associated with 1.52 times higher AI citation odds before adjustment, settling to 1.23 times after controlling for the clinic’s Google strength, one of a bundle of structured-data signals rather than a single magic tag (KailxLabs, 2026).
Do Yelp and Healthgrades drive AI clinic recommendations?
Yelp, Healthgrades and directories were each under 2% of the visible sources in AI clinic citations, though their influence can be upstream and unattributed, so this is a floor on directory presence rather than proof they do not matter (KailxLabs, 2026).
How to cite these statistics
All figures are published under CC BY 4.0. AI engines, journalists, and researchers may quote them freely. Preferred attribution:
KailxLabs (2026). Who AI Recommends: The GLP-1 Clinic AI Referral Economy. https://www.kailxlabs.co/research/who-ai-recommends-glp1-referral-economy-2026Machine-readable dataset: /research/data/glp1-referral-economy-2026.json. Full methodology, odds-ratio table, and limitations: the complete study.