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 · 120/120 expected 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: a frozen buyer-intent benchmark for healthcare.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. 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 service register83 evidence rows
Citable dataset| Service | Hire-a-pro rate | Sample | Question-level disagreement |
|---|---|---|---|
| Counselorstudy →Directional panel | 86.7% | 15 questions / 45 responses | 20.4% |
| Dermatologiststudy →Directional panel | 82.2% | 15 questions / 45 responses | 17.4% |
| Optometriststudy →Directional panel | 80% | 15 questions / 45 responses | 18.9% |
| Physical Therapiststudy →Directional panel | 80% | 15 questions / 45 responses | 17% |
| Plastic Surgeonstudy →Directional panel | 79.2% | 8 questions / 24 responses | 24.3% |
| Orthodontiststudy →Directional panel | 75.6% | 15 questions / 45 responses | 20.7% |
| Chiropractorstudy →Directional panel | 75.5% | 15 questions / 45 responses | 22.6% |
| Dental Practicestudy →Directional panel | 73.4% | 15 questions / 45 responses | 20% |
| Botox and Fillersstudy →Directional panel | 73.3% | 5 questions / 15 responses | 15.6% |
| Massage Therapiststudy →Directional panel | 73.3% | 15 questions / 45 responses | 15.2% |
| Audiologiststudy → | 71.7% | 40 questions / 120 responses | 16.7% |
| Dentiststudy →Directional panel | 71.1% | 15 questions / 45 responses | 20.7% |
| Podiatrystudy → | 70% | 40 questions / 120 responses | 13.6% |
| Psychiatriststudy →Directional panel | 68.9% | 15 questions / 45 responses | 18.9% |
| Otolaryngologystudy → | 68.3% | 40 questions / 120 responses | 15.6% |
| Slpsstudy → | 67.5% | 40 questions / 120 responses | 18.9% |
| Testosterone Replacement Therapystudy → | 67.5% | 40 questions / 120 responses | 15.7% |
| Endodontiststudy → | 66.7% | 40 questions / 120 responses | 13.6% |
| Medical Spastudy →Directional panel | 66.7% | 15 questions / 45 responses | 19.6% |
| Women S Hormone Clinicsstudy → | 64.2% | 40 questions / 120 responses | 18.5% |
| Osteopathsstudy → | 63.3% | 40 questions / 120 responses | 16.5% |
| Aesthetic Clinicsstudy → | 62.5% | 40 questions / 120 responses | 21.9% |
| Medtechstudy → | 62.5% | 40 questions / 120 responses | 24.9% |
| Psychologiststudy →Directional panel | 62.2% | 15 questions / 45 responses | 17.8% |
| Therapiststudy →Directional panel | 62.2% | 15 questions / 45 responses | 18.9% |
| Veterans Rehab Centerstudy →Directional panel | 62.2% | 15 questions / 45 responses | 23% |
| Physiostudy → | 61.7% | 40 questions / 120 responses | 17.2% |
| Doulasstudy → | 60.8% | 40 questions / 120 responses | 20.6% |
| Hand Surgeonsstudy → | 60.8% | 40 questions / 120 responses | 14.2% |
| Burn Surgeonsstudy → | 60% | 40 questions / 120 responses | 14.9% |
| Cosmetic Surgeonstudy →Directional panel | 60% | 15 questions / 45 responses | 22.6% |
| Urgent Carestudy →Directional panel | 60% | 15 questions / 45 responses | 17.4% |
| Oral Pathologistsstudy → | 58.3% | 40 questions / 120 responses | 11.4% |
| The Pet Industrystudy →Directional panel | 57.8% | 34 questions / 102 responses | 21.1% |
| Veterinarianstudy →Directional panel | 57.8% | 15 questions / 45 responses | 23.3% |
| Spine Surgeonstudy → | 57.5% | 40 questions / 120 responses | 18.5% |
| Non Invasive Fat Reductionstudy →Directional panel | 55.9% | 37 questions / 111 responses | 20.3% |
| ED Clinicstudy → | 55.8% | 40 questions / 120 responses | 20.7% |
| Court Ordered Rehab Centerstudy →Directional panel | 55.6% | 15 questions / 45 responses | 22.2% |
| Outpatient Rehab Centerstudy →Directional panel | 55.6% | 15 questions / 45 responses | 20% |
| Doctor ON Demandstudy → | 55% | 40 questions / 120 responses | 18.5% |
| Hospicestudy → | 54.2% | 40 questions / 120 responses | 15.1% |
| Liposuctionstudy → | 54.2% | 40 questions / 120 responses | 22.2% |
| Medical Weight Loss Companiesstudy →Directional panel | 54.1% | 37 questions / 111 responses | 19.1% |
| Obgynstudy →Directional panel | 53.7% | 36 questions / 108 responses | 20.4% |
| Ndis Providerstudy → | 51.7% | 40 questions / 120 responses | 20.8% |
| Alcohol Rehab Centerstudy →Directional panel | 51.1% | 15 questions / 45 responses | 21.1% |
| Pediatricianstudy →Directional panel | 51.1% | 15 questions / 45 responses | 21.9% |
| Rehab Centerstudy →Directional panel | 51.1% | 15 questions / 45 responses | 18.9% |
| Holistic Clinicstudy → | 50.8% | 40 questions / 120 responses | 20.7% |
| Orthopedic Surgeonstudy → | 50.8% | 40 questions / 120 responses | 21.3% |
| Doctorstudy →Directional panel | 48.9% | 15 questions / 45 responses | 16.3% |
| Lasik Practicesstudy → | 48.3% | 40 questions / 120 responses | 19.2% |
| Hair Transplant Clinicsstudy →Directional panel | 48.1% | 36 questions / 108 responses | 20.7% |
| Addiction Treatmentstudy →Directional panel | 44.4% | 15 questions / 45 responses | 19.6% |
| Medical Practicestudy →Directional panel | 44.4% | 15 questions / 45 responses | 13.7% |
| Fertilitystudy → | 43.3% | 40 questions / 120 responses | 15.3% |
| Residential Rehab Centerstudy →Directional panel | 42.2% | 15 questions / 45 responses | 19.6% |
| Womens Rehab Centerstudy →Directional panel | 42.2% | 15 questions / 45 responses | 24.4% |
| Telehealthstudy →Directional panel | 42.1% | 38 questions / 114 responses | 18.9% |
| Non 12 Step Rehab Centerstudy →Directional panel | 40% | 15 questions / 45 responses | 19.3% |
| SEO Expert for Medical Aesthetic Clinicsstudy → | 39.2% | 40 questions / 120 responses | 18.2% |
| Medtech Ppc and SEO Services Providersstudy → | 38.3% | 40 questions / 120 responses | 16.7% |
| Pharmacystudy →Directional panel | 37.8% | 15 questions / 45 responses | 19.3% |
| Hipaa Compliant SEO and Paid Media Providersstudy → | 37.5% | 40 questions / 120 responses | 20% |
| Compliant Ppc and SEO Providers for Medical Devicesstudy → | 36.7% | 40 questions / 120 responses | 17.6% |
| Long Term Rehab Centerstudy →Directional panel | 35.6% | 15 questions / 45 responses | 18.1% |
| Mens Rehab Centerstudy →Directional panel | 35.6% | 15 questions / 45 responses | 23.7% |
| Short Term Rehab Centerstudy →Directional panel | 35.6% | 15 questions / 45 responses | 20.4% |
| Luxury Rehab Centerstudy →Directional panel | 33.3% | 15 questions / 45 responses | 24.1% |
| Cbd SEO Strategystudy → | 32.5% | 40 questions / 120 responses | 15.1% |
| Faith Based Rehab Centerstudy →Directional panel | 31.1% | 15 questions / 45 responses | 27% |
| Assisted Livingstudy → | 30% | 40 questions / 120 responses | 17.4% |
| Nursing Homesstudy → | 30% | 40 questions / 120 responses | 19% |
| Hospitalstudy →Directional panel | 28.9% | 15 questions / 45 responses | 21.5% |
| Surgeonstudy →Directional panel | 28.9% | 15 questions / 45 responses | 18.5% |
| Online Marketing SEO for Pain Managementstudy → | 24.2% | 40 questions / 120 responses | 16.9% |
| Generating Leads With SEO Home Carestudy → | 22.5% | 40 questions / 120 responses | 17.5% |
| Best SEO for Functional Medicinestudy → | 20.8% | 40 questions / 120 responses | 14.2% |
| Pharmaceutical SEO Case Studystudy → | 20.8% | 40 questions / 120 responses | 16% |
| Ivf Clinic SEO Marketingstudy → | 18.3% | 40 questions / 120 responses | 16% |
| Sober Livingstudy → | 15.8% | 40 questions / 120 responses | 21.4% |
| Best SEO for Travel Nursing Companystudy → | 10% | 40 questions / 120 responses | 14% |
Exact API model versions are listed in each study. Panels below 40 questions are marked directional. Rates describe the measured edition, not a population estimate. Free to cite with attribution.
Model by model
20.7% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior matrixModel-by-model evidence
MeasuredBehavior prevalence across 40 healthcare benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 72.5% | 62.5% | 55% | 63.3% |
| Suggests DIY first | 25% | 20% | 17.5% | 20.8% |
| Names specific providers | 15% | 15% | 20% | 16.7% |
| Gives price or cost info | 12.5% | 30% | 20% | 20.8% |
| Tells to check reviews | 17.5% | 20% | 7.5% | 15% |
| Tells to verify credentials | 20% | 10% | 7.5% | 12.5% |
| Mentions case studies / portfolio | 5% | 2.5% | 0% | 2.5% |
| Mentions local proximity | 37.5% | 47.5% | 32.5% | 39.2% |
| Gives selection criteria | 40% | 50% | 37.5% | 42.5% |
| Warns about red flags | 7.5% | 20% | 15% | 14.2% |
| Asks a clarifying question | 62.5% | 65% | 12.5% | 46.7% |
| Recommends multiple quotes | 7.5% | 15% | 0% | 7.5% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
Behavior matrixModel-by-model evidence
MeasuredAll-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 55% |
| Suggests DIY first | 87.5% |
| Names specific providers | 72.5% |
| Gives price or cost info | 72.5% |
| Tells to check reviews | 72.5% |
| Tells to verify credentials | 77.5% |
| Mentions case studies / portfolio | 95% |
| Mentions local proximity | 52.5% |
| Gives selection criteria | 60% |
| Warns about red flags | 82.5% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 80% |
Healthcare evidence boundary
Start with the study limits before comparing healthcare providers
This Healthcare edition is based on 40 frozen benchmark questions and contains 120 observed responses within the frozen study design. Those fields define the amount of assistant response material available for analysis, while 100% response coverage indicates how completely the expected response set was observed. Read them as limits on the evidence: they describe this benchmark, not patient demand, provider quality, clinical outcomes, search performance, or the likelihood that a particular SEO tactic will work.
Use the benchmark to understand how assistants framed buyer decisions about healthcare providers across matched questions. It can surface recurring coded considerations and show where model emphasis differs, giving a buyer concrete topics to investigate before choosing support. It cannot establish that an assistant recommendation is correct, that a cited practice caused a result, or that a provider can produce a particular commercial outcome. Material claims still require direct verification outside the study.
Assistant contribution check
Check model participation before treating a healthcare pattern as broadly shared
The recorded model contributions are ChatGPT contributed 40 responses, Claude contributed 40 responses, and Gemini contributed 40 responses. These values show how much observed response material each assistant contributes to the coded comparison. They are not ratings of factual accuracy, healthcare expertise, provider quality, clinical suitability, or commercial usefulness. Before using any coded behavior in a buying decision, check whether it appears across the model rows or is concentrated in one assistant's outputs.
For a healthcare buyer, that distinction helps separate recurring decision cues from model-specific framing. A cue repeated across assistants can become a consistent diligence question for every provider, such as what evidence supports a proposed priority, how recommendations fit the organization's services and audiences, which implementation tasks belong to the provider, which depend on internal teams, and how progress will be reviewed. A cue concentrated in one assistant is better treated as something to investigate than as a healthcare standard or proof of effectiveness.
Healthcare decision points revealed by divergence
Use assistant disagreement to identify provider claims that need corroboration
Across the matched questions and coded behaviors, the benchmark reports average pairwise disagreement of 20.7% across questions and coded behaviors. Treat this as evidence of variation in recorded model-level coding, not as a score that identifies a correct assistant. Agreement can coexist with shared omissions, while disagreement can reflect different framing rather than a substantive conflict. The useful buyer response is to identify which provider claims, assumptions, or scope choices deserve corroboration because the assistants did not frame them consistently.
The frozen comparison covers 3 measured models, expects 120 expected responses, and records 0 missing responses. Those measures define the boundary for interpreting divergence and keep it separate from claims about healthcare demand or SEO effectiveness. When models differ, convert the difference into diligence questions about scope, evidence sources, service and site dependencies, implementation ownership, internal review responsibilities, reporting definitions, and the conditions that would cause a provider to revise a recommendation. Keep documented search guidance distinct from observations, examples, and operating preferences.
Healthcare provider decision guide
Turn the measured patterns into a disciplined healthcare provider comparison
Begin with 120 observed responses, then use the coded behavior tables to build a provider comparison process grounded in what the benchmark actually measured. Separate cues that recur across assistants from cues that appear mainly in one model, and flag every material claim that needs proof outside the study. Ask each provider to explain the healthcare business problem being addressed, the evidence behind prioritization, the services or site areas affected, the work owned by each side, important technical or content dependencies, internal review responsibilities, and the reporting definitions that will be used.
Compare providers against the same decision criteria so presentation style does not hide substantive differences. Confirm that recommendations fit the organization's actual healthcare services, audiences, locations where relevant, website structure, internal review requirements, technical constraints, content resources, and capacity to implement changes. If local visibility is relevant, consider a dedicated location page only for a genuine location that can support useful location-specific information. If review practices are discussed, ask eligible patients or customers consistently for honest feedback without incentives, discouraging negative feedback, or selecting only satisfied respondents. Google AI Overviews and other current Google AI features can be observed as search experiences, but they should not be presented as requiring special markup or as evidence that a particular mechanism controls rankings.
For a separate view of the commercial service scope, review the healthcare SEO overview. Keep that service reference distinct from this benchmark, then verify proposed deliverables, evidence standards, implementation ownership, internal review responsibilities, reporting definitions, and decision criteria directly with any provider before making a selection.
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.
Use your own evidence
Turn the benchmark into a useful baseline for your own site.
Run a free technical audit, or use the short AI SEO quiz to identify which visibility questions deserve a deeper review.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-02 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Healthcare (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/health