Original research · 2026-07 edition

AI SEO Statistics: Dermatologist (2026-07 edition)

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

The question bank

The questions we tested — a frozen buyer-intent benchmark for dermatologist.

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.

I have a mole that's changed color recently, should I go to urgent care or wait for a dermatologist appointment?
What is the average out-of-pocket cost for a full-body skin cancer screening in a major city?
Is it better to see a dermatologist or a med spa for professional chemical peels?
My teenager has severe back acne that won't go away with drugstore washes, is it time for a specialist?
What questions should I ask during a consultation to see if a dermatologist is experienced with skin of color?
How can I tell if a dermatologist is board-certified and why does that matter for my biopsy?
Are there any specific red flags I should look for in online reviews for a cosmetic dermatology clinic?
Does insurance typically cover the removal of skin tags if they are getting caught on my clothes?
Show all 15 questions
I've been using retinol for six months with no results for my fine lines, what's the next level of treatment a doctor would suggest?
Can a dermatologist help with hair loss, or do I need a different kind of doctor?
How do I choose between a general dermatologist and one that specializes in Mohs surgery?
I need a dermatologist who can see me this week for a painful cyst, what’s the best way to find a last-minute opening?
What is the difference in results between a prescription-strength cream from a doctor and high-end department store skincare?
Are virtual dermatology visits actually effective for diagnosing a suspicious rash or do they need to see it in person?
If I want to start a preventative anti-aging routine, is it worth paying for a professional consultation or should I just follow social media advice?

Model by model

17.4% 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
Measured

Behavior prevalence across 15 dermatologist benchmark questions, 2026-07 edition. Last column: equal-model mean.

Behavior prevalence across 15 dermatologist benchmark questions, 2026-07 edition. Last column: equal-model mean.
BehaviorChatGPTClaudeGeminiEqual-model mean
Recommends hiring a professional86.7%86.7%73.3%82.2%
Suggests DIY first13.3%0%0%4.4%
Names specific providers0%6.7%6.7%4.5%
Gives price or cost info6.7%13.3%6.7%8.9%
Tells to check reviews6.7%6.7%13.3%8.9%
Tells to verify credentials40%26.7%20%28.9%
Mentions case studies / portfolio20%6.7%0%8.9%
Mentions local proximity13.3%13.3%6.7%11.1%
Gives selection criteria53.3%40%40%44.4%
Warns about red flags26.7%20%6.7%17.8%
Asks a clarifying question86.7%60%0%48.9%
Recommends multiple quotes13.3%6.7%0%6.7%

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
Measured

All-model binary agreement by behavior across 15 benchmark questions.

All-model binary agreement by behavior across 15 benchmark questions.
BehaviorAll-model agreement
Recommends hiring a professional66.7%
Suggests DIY first86.7%
Names specific providers93.3%
Gives price or cost info93.3%
Tells to check reviews80%
Tells to verify credentials73.3%
Mentions case studies / portfolio80%
Mentions local proximity93.3%
Gives selection criteria60%
Warns about red flags66.7%
Asks a clarifying question6.7%
Recommends multiple quotes86.7%

By model

How each assistant handled Dermatologist questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same dermatologist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 73.3% (Gemini), a 13-point gap on an identical question set.

Across the 15 dermatologist answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 86.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 40%, averaging 458 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 dermatologist answers it produced, Claude recommended hiring a professional in 86.7% of them and suggested a DIY approach first 0% 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. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 274 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 dermatologist answers it produced, Gemini recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% 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 6.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 20%, averaging 312 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 6.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a dermatologist buyer to a professional (86.7%) and Gemini the least (73.3%). ChatGPT produced the longest answers, at 458 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.

Question-level model disagreement is 17.4% — 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 which assistant a dermatologist buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 86.7% (ChatGPT) — a 87-point spread.
  • Tells the buyer to verify credentials: from 20% (Gemini) to 40% (ChatGPT) — a 20-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
  • Warns about red flags or scams: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.
  • Recommends hiring a professional: from 73.3% (Gemini) to 86.7% (ChatGPT) — a 13-point spread.

The widest single gap — asks a clarifying question, 87 points — means a dermatologist 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 dermatologist market.

Where they agree

The points of near-consensus in Dermatologist.

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

  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions local proximity: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 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 Dermatologist, averaged across the three models.

The behaviors AI models reproduce most often for dermatologist are recommends hiring a professional (82.2% on average), asks a clarifying question (48.9%) and gives selection criteria (44.4%); the rarest are suggests a DIY approach first (4.4%), names a specific provider (4.5%) and recommends multiple quotes (6.7%). 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:

  • Recommends hiring a professional: 82.2% on average (ChatGPT 86.7%, Claude 86.7%, Gemini 73.3%) — a 13-point spread.
  • Asks a clarifying question: 48.9% on average (ChatGPT 86.7%, Claude 60%, Gemini 0%) — a 87-point spread.
  • Gives selection criteria: 44.4% on average (ChatGPT 53.3%, Claude 40%, Gemini 40%) — a 13-point spread.
  • Tells the buyer to verify credentials: 28.9% on average (ChatGPT 40%, Claude 26.7%, Gemini 20%) — a 20-point spread.
  • Warns about red flags or scams: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
  • Mentions local proximity: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Mentions case studies or portfolio: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Recommends multiple quotes: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Suggests a DIY approach first: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the dermatologist buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the dermatologist buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 28.9%. Warning about red flags or scams appeared in 17.8%.

On structuring the decision, a selection-criteria checklist showed up in 44.4% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for dermatologist is "recommends multiple quotes" at 6.7% 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 Dermatologist providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 dermatologist 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 dermatologist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Dermatologist questions cover.

The 15 questions behind every percentage on this page form a frozen dermatologist (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 dermatologist 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 dermatologist 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 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 2026-07-04 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: Dermatologist (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/dermatologist