AI models are highly reluctant to act as direct referral engines in healthcare. With specific providers named in only 17% of responses, healthcare marketers must focus on appearing in the 'selection criteria' (which appear in 43% of responses) rather than relying on direct brand mentions.
Original research · 2026-07 edition
AI SEO Statistics: Healthcare (2026-07 edition)
In the healthcare sector, AI models demonstrate a cautious approach, rarely recommending specific providers (17% average) and frequently advising users to consult a professional (63% average). Instead of direct referrals, models like Claude and ChatGPT prefer to guide users by asking clarifying questions and providing criteria for selecting a doctor. For healthcare organizations, AI visibility hinges on aligning with these selection criteria and optimizing for local proximity, which remains a key factor in AI-generated advice.
40 questions · 120 AI responses · 3 models · measured 2026-07-02
Key statistics
Every number below is measured, anchored, and sourced.
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
MeasuredAI SEO Statistics — Healthcare, 2026-07
The question bank
The questions we tested — sampled from real buyer journeys in healthcare.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
What is the best clinic for [condition] near me?
Which dentists accept [Insurance Name] in [City]?
How much does [treatment] cost without insurance?
What are the symptoms of [condition] and when should I see a doctor?
Are there any top-rated physical therapists for back pain nearby?
Why does my knee make a popping sound every time I squat and should I get it checked?
Is it worth seeing a specialist for adult acne or can a general practitioner handle it?
How can I tell if a mole needs to be biopsied by a dermatologist or if it is just a freckle?
Show all 40 questions
What are the red flags I should look for when visiting a new chiropractor for the first time?
I do not have insurance, where can I find low-cost mental health counseling for anxiety in my area?
What is the difference in cost and recovery time between LASIK and PRK eye surgery?
How do I find a primary care doctor who is actually taking new patients and has availability this month?
Is it cheaper to pay cash for a routine blood panel or go through my high-deductible insurance plan?
What specific questions should I ask a surgeon during a consultation for a total knee replacement?
Can I treat a suspected UTI with over-the-counter meds or do I definitely need a prescription from a clinic?
How do I verify a dentist's credentials and see if they have any history of patient complaints?
What is the typical wait time for a non-emergency appointment with a local rheumatologist?
I am feeling completely burnt out; should I look for a psychologist or a psychiatrist for help?
Are there any clinics nearby that offer evening or weekend hours for physical therapy sessions?
How much does a standard teeth cleaning and X-ray cost for someone paying out of pocket?
What are the signs that a physical therapist is actually helping my recovery versus just going through the motions?
Find a doctor who specializes in thyroid issues and is known for listening to patient concerns about fatigue.
Should I go to an urgent care center or wait for my doctor if I think I have the flu but no fever?
How do I get a second opinion on a recommended surgery without offending my current specialist?
What is the typical process and timeline for getting a referral to a cardiologist?
Are there any holistic or functional medicine doctors in my city who treat chronic gut health issues?
What should I expect to pay for a first-time consultation with a licensed nutritionist?
How do I know if a local medical spa is reputable and safe for getting Botox injections?
What are the pros and cons of using an online therapy platform versus seeing an in-person therapist?
Is there a way to get a firm price estimate for a gallbladder removal before I schedule the procedure?
I have a 3000 dollar deductible, how can I find the most affordable imaging center for a CT scan?
What are the warning signs of a bad pediatric dentist that I should watch out for with my toddler?
Can a general practitioner diagnose sleep apnea or do I need to go to a specialized sleep study clinic?
How do I find a primary care doctor who is specifically supportive of LGBTQ+ healthcare needs?
What is the functional difference between seeing a nurse practitioner and a doctor for routine checkups?
Where can I find a local therapist who offers a sliding scale fee based on my current income?
Is it better to see an orthopedic surgeon or a sports medicine doctor for a suspected torn ligament?
How do I handle a medical bill that came back much higher than the original estimate I was given?
What are the best questions to ask when interviewing a new midwife or OBGYN for prenatal care?
Why is my dental insurance only covering half the cost of my crown and how can I appeal the decision?
By service
Not all healthcare services are treated the same by AI.
We ran the same measurement on 83 distinct healthcare services. The rate at which ChatGPT, Claude and Gemini push buyers toward a professional swings widely, and that gap is exactly where authority is won or lost.
Measured across ChatGPT, Claude and Gemini · standardized buyer questions per service × 3 models · Authority Specialist AI Study. Free to cite with attribution.
Model by model
21-point average divergence: which AI you ask changes the answer.
The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about healthcare buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 73% | 63% | 55% | 55% |
| Suggests DIY first | 25% | 20% | 18% | 88% |
| Names specific providers | 15% | 15% | 20% | 73% |
| Gives price or cost info | 13% | 30% | 20% | 73% |
| Tells to check reviews | 18% | 20% | 8% | 73% |
| Tells to verify credentials | 20% | 10% | 8% | 78% |
| Mentions case studies / portfolio | 5% | 3% | 0% | 95% |
| Mentions local proximity | 38% | 48% | 33% | 53% |
| Gives selection criteria | 40% | 50% | 38% | 60% |
| Warns about red flags | 8% | 20% | 15% | 83% |
| Asks a clarifying question | 63% | 65% | 13% | 20% |
| Recommends multiple quotes | 8% | 15% | 0% | 80% |
By model
How each assistant handled Healthcare questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same healthcare questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 72.5% (ChatGPT) down to 55% (Gemini), a 18-point gap on an identical question set.
Across the 40 healthcare answers it produced, ChatGPT recommended hiring a professional in 72.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 15% of answers (about 0.4 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 20%, averaging 471 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 37.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 healthcare answers it produced, Claude recommended hiring a professional in 62.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 15% of answers (about 1 distinct providers per answer) and included price or cost information 30% of the time. Claude asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 10%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 47.5%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 15%.
Across the 40 healthcare answers it produced, Gemini recommended hiring a professional in 55% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 20% of answers (about 0.7 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 12.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 7.5%, averaging 268 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a healthcare buyer to a professional (72.5%) and Gemini the least (55%). ChatGPT produced the longest answers, at 471 words on average. Specific providers were named most often by Gemini (20%) — even there, roughly one answer in 5 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 20.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a healthcare buyer happens to ask matters most:
- Asks a clarifying question: from 12.5% (Gemini) to 65% (Claude) — a 53-point spread.
- Recommends hiring a professional: from 55% (Gemini) to 72.5% (ChatGPT) — a 18-point spread.
- Gives price or cost information: from 12.5% (ChatGPT) to 30% (Claude) — a 18-point spread.
- Mentions local proximity: from 32.5% (Gemini) to 47.5% (Claude) — a 15-point spread.
- Recommends multiple quotes: from 0% (Gemini) to 15% (Claude) — a 15-point spread.
The widest single gap — asks a clarifying question, 53 points — means a healthcare buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the healthcare market.
Where they agree
The points of near-consensus in Healthcare.
On other behaviors the three models move almost in lockstep — the points of near-consensus for healthcare, where all three landed within a few points of each other:
- Names a specific provider: 15%–20% across all three (a 5-point spread).
- Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
- Suggests a DIY approach first: 17.5%–25% across all three (a 8-point spread).
- Tells the buyer to check reviews: 7.5%–20% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Healthcare, averaged across the three models.
The behaviors AI models reproduce most often for healthcare are recommends hiring a professional (63.3% on average), asks a clarifying question (46.7%) and gives selection criteria (42.5%); the rarest are mentions case studies or portfolio (2.5%), recommends multiple quotes (7.5%) and tells the buyer to verify credentials (12.5%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 63.3% on average (ChatGPT 72.5%, Claude 62.5%, Gemini 55%) — a 18-point spread.
- Asks a clarifying question: 46.7% on average (ChatGPT 62.5%, Claude 65%, Gemini 12.5%) — a 53-point spread.
- Gives selection criteria: 42.5% on average (ChatGPT 40%, Claude 50%, Gemini 37.5%) — a 13-point spread.
- Mentions local proximity: 39.2% on average (ChatGPT 37.5%, Claude 47.5%, Gemini 32.5%) — a 15-point spread.
- Suggests a DIY approach first: 20.8% on average (ChatGPT 25%, Claude 20%, Gemini 17.5%) — a 8-point spread.
- Gives price or cost information: 20.8% on average (ChatGPT 12.5%, Claude 30%, Gemini 20%) — a 18-point spread.
- Names a specific provider: 16.7% on average (ChatGPT 15%, Claude 15%, Gemini 20%) — a 5-point spread.
- Tells the buyer to check reviews: 15% on average (ChatGPT 17.5%, Claude 20%, Gemini 7.5%) — a 13-point spread.
- Warns about red flags or scams: 14.2% on average (ChatGPT 7.5%, Claude 20%, Gemini 15%) — a 13-point spread.
- Tells the buyer to verify credentials: 12.5% on average (ChatGPT 20%, Claude 10%, Gemini 7.5%) — a 13-point spread.
- Recommends multiple quotes: 7.5% on average (ChatGPT 7.5%, Claude 15%, Gemini 0%) — a 15-point spread.
- Mentions case studies or portfolio: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%) — a 5-point spread.
Trust signals
How well the models protect the healthcare buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the healthcare buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 14.2%.
On structuring the decision, a selection-criteria checklist showed up in 42.5% of answers on average and a recommendation to gather multiple quotes in 7.5%. The single least-reproduced protective signal for healthcare is "recommends multiple quotes" at 7.5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Healthcare providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 healthcare answers, a specific provider was named in 16.7% of responses on average — roughly 0.7 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for healthcare: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
When a name did surface, 120 stored responses were scanned for brand and organization mentions. The most frequently named were:
- Zocdoc: 19 mentions (15.8% of responses).
- Healthgrades: 18 mentions (15% of responses).
- Yelp: 10 mentions (8.3% of responses).
- Google Maps: 9 mentions (7.5% of responses).
- Psychology Today: 6 mentions (5% of responses).
- Healthcare Bluebook: 5 mentions (4.2% of responses).
- Vitals: 5 mentions (4.2% of responses).
- Google: 5 mentions (4.2% of responses).
- Aetna: 4 mentions (3.3% of responses).
- Open Path Collective: 4 mentions (3.3% of responses).
Mention frequency in stored AI responses. A mention is not an endorsement.
The question set
What these 40 Healthcare questions cover.
The 40 questions behind every percentage on this page were drawn from real healthcare services (clinics, dentists, physicians, therapy) buyer journeys, expanded from 5 seed prompts. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact healthcare question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-02, the figures describe this specific healthcare question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
What this means
What this means for healthcare businesses.
The high rate of clarifying questions from ChatGPT (63%) and Claude (65%) means users are often guided through a multi-prompt diagnostic or triage journey. Healthcare content should be structured to answer these specific, long-tail follow-up questions rather than just broad top-of-funnel queries.
Local SEO remains relevant in AI search. With models mentioning local proximity in nearly 40% of responses, maintaining accurate location data and localized content is critical for being part of the AI's recommended evaluation criteria.
AI visibility is measurable. We just measured it for your industry.
Open your dashboard to see how ChatGPT, Claude and Gemini describe YOUR business — mentions, recommendations, citations, gaps.
Methodology
A controlled snapshot, documented end to end.
40 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-02, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →