AI SEO Statistics: Spine Surgeon (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in spine surgeon.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
19-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 spine surgeon buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 73% | 63% | 38% | 50% |
| Suggests DIY first | 8% | 8% | 0% | 90% |
| Names specific providers | 5% | 5% | 10% | 85% |
| Gives price or cost info | 5% | 5% | 5% | 100% |
| Tells to check reviews | 15% | 10% | 5% | 85% |
| Tells to verify credentials | 25% | 23% | 10% | 73% |
| Mentions case studies / portfolio | 13% | 3% | 0% | 85% |
| Mentions local proximity | 20% | 20% | 15% | 80% |
| Gives selection criteria | 45% | 38% | 33% | 58% |
| Warns about red flags | 18% | 10% | 10% | 80% |
| Asks a clarifying question | 60% | 58% | 3% | 20% |
| Recommends multiple quotes | 28% | 18% | 8% | 63% |
By model
How each assistant handled Spine Surgeon questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same spine surgeon 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 37.5% (Gemini), a 35-point gap on an identical question set.
Across the 40 spine surgeon answers it produced, ChatGPT recommended hiring a professional in 72.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 5% of answers (about 0 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 25%, averaging 502 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 27.5%.
Across the 40 spine surgeon answers it produced, Claude recommended hiring a professional in 62.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 5% of the time. Claude asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 22.5%, averaging 296 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 17.5%.
Across the 40 spine surgeon answers it produced, Gemini recommended hiring a professional in 37.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 5% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 10%, averaging 283 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 7.5%.
Taken together, ChatGPT is the assistant most likely to route a spine surgeon buyer to a professional (72.5%) and Gemini the least (37.5%). ChatGPT produced the longest answers, at 502 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 18.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a spine surgeon buyer happens to ask matters most:
- Asks a clarifying question: from 2.5% (Gemini) to 60% (ChatGPT) — a 58-point spread.
- Recommends hiring a professional: from 37.5% (Gemini) to 72.5% (ChatGPT) — a 35-point spread.
- Recommends multiple quotes: from 7.5% (Gemini) to 27.5% (ChatGPT) — a 20-point spread.
- Tells the buyer to verify credentials: from 10% (Gemini) to 25% (ChatGPT) — a 15-point spread.
- Mentions case studies or portfolio: from 0% (Gemini) to 12.5% (ChatGPT) — a 13-point spread.
The widest single gap — asks a clarifying question, 58 points — means a spine surgeon 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 spine surgeon market.
Where they agree
The points of near-consensus in Spine Surgeon.
On other behaviors the three models move almost in lockstep — the points of near-consensus for spine surgeon, where all three landed within a few points of each other:
- Gives price or cost information: 5% across all three models.
- Names a specific provider: 5%–10% across all three (a 5-point spread).
- Mentions local proximity: 15%–20% across all three (a 5-point spread).
- Suggests a DIY approach first: 0%–7.5% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "gives price or cost information" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Spine Surgeon, averaged across the three models.
The behaviors AI models reproduce most often for spine surgeon are recommends hiring a professional (57.5% on average), asks a clarifying question (40%) and gives selection criteria (38.3%); the rarest are mentions case studies or portfolio (5%), gives price or cost information (5%) and suggests a DIY approach first (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: 57.5% on average (ChatGPT 72.5%, Claude 62.5%, Gemini 37.5%) — a 35-point spread.
- Asks a clarifying question: 40% on average (ChatGPT 60%, Claude 57.5%, Gemini 2.5%) — a 58-point spread.
- Gives selection criteria: 38.3% on average (ChatGPT 45%, Claude 37.5%, Gemini 32.5%) — a 13-point spread.
- Tells the buyer to verify credentials: 19.2% on average (ChatGPT 25%, Claude 22.5%, Gemini 10%) — a 15-point spread.
- Mentions local proximity: 18.3% on average (ChatGPT 20%, Claude 20%, Gemini 15%) — a 5-point spread.
- Recommends multiple quotes: 17.5% on average (ChatGPT 27.5%, Claude 17.5%, Gemini 7.5%) — a 20-point spread.
- Warns about red flags or scams: 12.5% on average (ChatGPT 17.5%, Claude 10%, Gemini 10%) — a 8-point spread.
- Tells the buyer to check reviews: 10% on average (ChatGPT 15%, Claude 10%, Gemini 5%) — a 10-point spread.
- Names a specific provider: 6.7% on average (ChatGPT 5%, Claude 5%, Gemini 10%) — a 5-point spread.
- Suggests a DIY approach first: 5% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 0%) — a 8-point spread.
- Gives price or cost information: 5% on average (ChatGPT 5%, Claude 5%, Gemini 5%).
- Mentions case studies or portfolio: 5% on average (ChatGPT 12.5%, Claude 2.5%, Gemini 0%) — a 13-point spread.
Trust signals
How well the models protect the spine surgeon buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the spine surgeon buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10% of answers on average. Verifying credentials or certifications appeared in 19.2%. Warning about red flags or scams appeared in 12.5%.
On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 17.5%. The single least-reproduced protective signal for spine surgeon is "tells the buyer to check reviews" at 10% 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 Spine Surgeon providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 spine surgeon answers, a specific provider was named in 6.7% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for spine surgeon: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Spine Surgeon questions cover.
The 40 questions behind every percentage on this page were drawn from real spine surgeon (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact spine surgeon 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 2026-07-06, the figures describe this specific spine surgeon question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
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 2026-07-06, 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 →