AI SEO Statistics: Lasik Practices (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your lasik practices SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for lasik practices.
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.
Show all 40 questions
Model by model
19.2% 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 prevalence across 40 lasik practices benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 70% | 57.5% | 17.5% | 48.3% |
| Suggests DIY first | 2.5% | 0% | 2.5% | 1.7% |
| Names specific providers | 0% | 2.5% | 2.5% | 1.7% |
| Gives price or cost info | 5% | 12.5% | 15% | 10.8% |
| Tells to check reviews | 12.5% | 7.5% | 0% | 6.7% |
| Tells to verify credentials | 22.5% | 12.5% | 2.5% | 12.5% |
| Mentions case studies / portfolio | 12.5% | 2.5% | 0% | 5% |
| Mentions local proximity | 10% | 5% | 0% | 5% |
| Gives selection criteria | 42.5% | 42.5% | 15% | 33.3% |
| Warns about red flags | 15% | 22.5% | 15% | 17.5% |
| Asks a clarifying question | 57.5% | 60% | 2.5% | 40% |
| Recommends multiple quotes | 10% | 22.5% | 0% | 10.8% |
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.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 30% |
| Suggests DIY first | 95% |
| Names specific providers | 97.5% |
| Gives price or cost info | 82.5% |
| Tells to check reviews | 85% |
| Tells to verify credentials | 72.5% |
| Mentions case studies / portfolio | 85% |
| Mentions local proximity | 87.5% |
| Gives selection criteria | 47.5% |
| Warns about red flags | 82.5% |
| Asks a clarifying question | 17.5% |
| Recommends multiple quotes | 72.5% |
By model
How each assistant handled Lasik Practices questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same lasik practices questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 70% (ChatGPT) down to 17.5% (Gemini), a 53-point gap on an identical question set.
Across the 40 lasik practices answers it produced, ChatGPT recommended hiring a professional in 70% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 0% 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 57.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 22.5%, averaging 491 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 lasik practices answers it produced, Claude recommended hiring a professional in 57.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 12.5% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 12.5%, averaging 283 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 2.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 22.5%.
Across the 40 lasik practices answers it produced, Gemini recommended hiring a professional in 17.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 2.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 2.5%, averaging 248 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching lasik practices toward professional help (70%) and Gemini the least (17.5%). ChatGPT produced the longest answers, at 491 words on average. Specific providers were named most often by Claude (2.5%). Even there, roughly one answer in 40 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 19.2%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching lasik practices:
- Asks a clarifying question: from 2.5% (Gemini) to 60% (Claude). The spread is 58 points.
- Recommends hiring a professional: from 17.5% (Gemini) to 70% (ChatGPT). The spread is 53 points.
- Gives selection criteria: from 15% (Gemini) to 42.5% (ChatGPT). The spread is 28 points.
- Recommends multiple quotes: from 0% (Gemini) to 22.5% (Claude). The spread is 23 points.
- Tells the buyer to verify credentials: from 2.5% (Gemini) to 22.5% (ChatGPT). The spread is 20 points.
The widest single gap concerns asks a clarifying question at 58 points. This means a buyer researching lasik practices 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 lasik practices market.
Where they agree
The points of near-consensus in Lasik Practices.
On other behaviors the three models move almost in lockstep. The points of near-consensus for lasik practices, where all three landed within a few points of each other:
- Suggests a DIY approach first: 0%–2.5% across all three (a 3-point spread).
- Names a specific provider: 0%–2.5% across all three (a 3-point spread).
- Warns about red flags or scams: 15%–22.5% across all three (a 8-point spread).
- Gives price or cost information: 5%–15% across all three (a 10-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 97.5% of questions) and least consistently on "asks a clarifying question" (17.5%).
Every behavior, measured
All twelve coded behaviors for Lasik Practices, averaged across the three models.
The behaviors AI models reproduce most often for lasik practices are recommends hiring a professional (48.3% on average), asks a clarifying question (40%) and gives selection criteria (33.3%); the rarest are names a specific provider (1.7%), suggests a DIY approach first (1.7%) and mentions local proximity (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: 48.3% on average (ChatGPT 70%, Claude 57.5%, Gemini 17.5%). The spread is 53 points.
- Asks a clarifying question: 40% on average (ChatGPT 57.5%, Claude 60%, Gemini 2.5%). The spread is 58 points.
- Gives selection criteria: 33.3% on average (ChatGPT 42.5%, Claude 42.5%, Gemini 15%). The spread is 28 points.
- Warns about red flags or scams: 17.5% on average (ChatGPT 15%, Claude 22.5%, Gemini 15%). The spread is 8 points.
- Tells the buyer to verify credentials: 12.5% on average (ChatGPT 22.5%, Claude 12.5%, Gemini 2.5%). The spread is 20 points.
- Gives price or cost information: 10.8% on average (ChatGPT 5%, Claude 12.5%, Gemini 15%). The spread is 10 points.
- Recommends multiple quotes: 10.8% on average (ChatGPT 10%, Claude 22.5%, Gemini 0%). The spread is 23 points.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 12.5%, Claude 7.5%, Gemini 0%). The spread is 13 points.
- Mentions case studies or portfolio: 5% on average (ChatGPT 12.5%, Claude 2.5%, Gemini 0%). The spread is 13 points.
- Mentions local proximity: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%). The spread is 10 points.
- Suggests a DIY approach first: 1.7% on average (ChatGPT 2.5%, Claude 0%, Gemini 2.5%). The spread is 3 points.
- Names a specific provider: 1.7% on average (ChatGPT 0%, Claude 2.5%, Gemini 2.5%). The spread is 3 points.
Trust signals
How well the models protect the lasik practices buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the lasik practices buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 17.5%.
On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 10.8%. The single least-reproduced protective signal for lasik practices is "tells the buyer to check reviews" at 6.7% on average. This is 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 Lasik Practices providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 lasik practices answers, a specific provider was named in 1.7% of responses on average, or roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for lasik practices: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Lasik Practices questions cover.
The 40 questions behind every percentage on this page form a frozen lasik practices (healthcare services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact lasik practices question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific lasik practices 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 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 2026-07-06 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: Lasik Practices (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/lasik-practices