AI SEO Statistics: Doctor ON Demand (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same doctor on demand questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 47.5% (Gemini), a 13-point gap on an identical question set.

Across the 40 doctor on demand answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 17.5% of answers (about 0.6 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 15%, and told the buyer to verify credentials in 20%, averaging 418 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 32.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 doctor on demand answers it produced, Claude recommended hiring a professional in 57.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 42.5% of answers (about 1.5 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 10%, averaging 274 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 doctor on demand answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 57.5% of answers (about 2 distinct providers per answer) and included price or cost information 10% of the time. Gemini asked a clarifying question before answering in 5% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 10%, averaging 314 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 27.5%; 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 doctor on demand buyer to a professional (60%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 418 words on average. Specific providers were named most often by Gemini (57.5%) — even there, roughly one answer in 2 carried a name.

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

• Mentions case studies or portfolio: 0% across all three models.
• Recommends multiple quotes: 0%–2.5% across all three (a 3-point spread).
• Gives price or cost information: 5%–10% across all three (a 5-point spread).
• Mentions local proximity: 27.5%–32.5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (15%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the doctor on demand buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 10.8%.

On structuring the decision, a selection-criteria checklist showed up in 29.2% of answers on average and a recommendation to gather multiple quotes in 0.8%. The single least-reproduced protective signal for doctor on demand is "recommends multiple quotes" at 0.8% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real doctor on demand (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 doctor on demand 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.