Original research · 2026-07 edition

AI SEO Statistics: Pediatrician (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in pediatrician.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

What is the difference between taking my baby to a family practitioner versus a specialized pediatrician?
I am expecting in two months, when is the right time to start interviewing pediatricians and what should I ask them?
How do I know if a pediatrician's office is actually pro-vaccine or if they allow alternative schedules?
My current pediatrician never sees us on time and the waiting room is always packed, is this normal or should I look for a new one?
What are the red flags I should look for during a first meet and greet visit with a new kids' doctor?
If my toddler has a high fever at 2 AM, does a typical pediatrician have an after-hours line or do I just go to the ER?
Are there any pediatricians that offer concierge services where I can text the doctor directly, and how much does that usually cost?
How do I check a pediatrician's board certification and see if they have had any past malpractice claims?
Show all 15 questions
I am moving to a new city; what is the best way to find a pediatrician who specializes in neurodivergent children or ADHD?
Do most pediatric offices charge a new patient fee if I am just coming in for a consultation before my baby is born?
Is it better to choose a solo practitioner who knows us well or a large group where we might see a different doctor every time?
What should I do if my insurance isn't accepted by the top-rated pediatrician in my area?
Can a pediatrician help with behavioral issues and sleep training, or do I need a different kind of specialist for that?
My child is 13 now, should I be looking for a pediatrician who has experience with adolescent health and puberty?
How can I tell if a pediatrician's office is clean and follows good sanitization protocols for the sick vs well sides?

Model by model

22-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 pediatrician buyers.

Behavior rates across 15 pediatrician buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%40%40%67%
Suggests DIY first27%7%7%80%
Names specific providers0%7%7%93%
Gives price or cost info13%13%13%80%
Tells to check reviews20%27%13%67%
Tells to verify credentials20%13%27%67%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity47%27%27%40%
Gives selection criteria73%87%60%60%
Warns about red flags33%20%20%60%
Asks a clarifying question80%73%0%7%
Recommends multiple quotes7%0%0%93%

By model

How each assistant handled Pediatrician questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same pediatrician questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 40% (Claude), a 33-point gap on an identical question set.

Across the 15 pediatrician answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 20%, averaging 518 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 pediatrician answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 294 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 86.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 pediatrician answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 271 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a pediatrician buyer to a professional (73.3%) and Claude the least (40%). ChatGPT produced the longest answers, at 518 words on average. Specific providers were named most often by Claude (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a pediatrician buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
  • Recommends hiring a professional: from 40% (Claude) to 73.3% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 60% (Gemini) to 86.7% (Claude) — a 27-point spread.
  • Suggests a DIY approach first: from 6.7% (Claude) to 26.7% (ChatGPT) — a 20-point spread.
  • Mentions local proximity: from 26.7% (Claude) to 46.7% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 80 points — means a pediatrician 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 pediatrician market.

Where they agree

The points of near-consensus in Pediatrician.

On other behaviors the three models move almost in lockstep — the points of near-consensus for pediatrician, where all three landed within a few points of each other:

  • Gives price or cost information: 13.3% across all three models.
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-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 93.3% of questions) and least consistently on "asks a clarifying question" (6.7%).

Every behavior, measured

All twelve coded behaviors for Pediatrician, averaged across the three models.

The behaviors AI models reproduce most often for pediatrician are gives selection criteria (73.3% on average), recommends hiring a professional (51.1%) and asks a clarifying question (51.1%); the rarest are recommends multiple quotes (2.2%), mentions case studies or portfolio (2.2%) and names a specific provider (4.5%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 73.3% on average (ChatGPT 73.3%, Claude 86.7%, Gemini 60%) — a 27-point spread.
  • Recommends hiring a professional: 51.1% on average (ChatGPT 73.3%, Claude 40%, Gemini 40%) — a 33-point spread.
  • Asks a clarifying question: 51.1% on average (ChatGPT 80%, Claude 73.3%, Gemini 0%) — a 80-point spread.
  • Mentions local proximity: 33.4% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 26.7%) — a 20-point spread.
  • Warns about red flags or scams: 24.4% on average (ChatGPT 33.3%, Claude 20%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 20%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 20%, Claude 13.3%, Gemini 26.7%) — a 13-point spread.
  • Suggests a DIY approach first: 13.4% on average (ChatGPT 26.7%, Claude 6.7%, Gemini 6.7%) — a 20-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Names a specific provider: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the pediatrician buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the pediatrician buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 24.4%.

On structuring the decision, a selection-criteria checklist showed up in 73.3% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for pediatrician is "recommends multiple quotes" at 2.2% 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 Pediatrician providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 pediatrician answers, a specific provider was named in 4.5% 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 pediatrician: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Pediatrician questions cover.

The 15 questions behind every percentage on this page were drawn from real pediatrician (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 pediatrician 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 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific pediatrician 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.

15 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 2026-07-04, 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 →