AI SEO Statistics: Medical Malpractice Attorneys (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 medical malpractice attorneys.
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
21-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 medical malpractice attorneys buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 83% | 83% | 40% | 38% |
| Suggests DIY first | 10% | 5% | 3% | 93% |
| Names specific providers | 0% | 0% | 0% | 100% |
| Gives price or cost info | 15% | 40% | 18% | 53% |
| Tells to check reviews | 15% | 10% | 3% | 83% |
| Tells to verify credentials | 18% | 15% | 3% | 80% |
| Mentions case studies / portfolio | 13% | 15% | 3% | 80% |
| Mentions local proximity | 45% | 20% | 10% | 50% |
| Gives selection criteria | 25% | 28% | 18% | 68% |
| Warns about red flags | 13% | 15% | 10% | 85% |
| Asks a clarifying question | 80% | 73% | 3% | 15% |
| Recommends multiple quotes | 13% | 5% | 0% | 83% |
By model
How each assistant handled Medical Malpractice Attorneys questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same medical malpractice attorneys questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 82.5% (ChatGPT) down to 40% (Gemini), a 43-point gap on an identical question set.
Across the 40 medical malpractice attorneys answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 15% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 17.5%, averaging 522 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 45%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 12.5%.
Across the 40 medical malpractice attorneys answers it produced, Claude recommended hiring a professional in 82.5% of them and suggested a DIY approach first 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 40% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 15%, averaging 317 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 15%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 5%.
Across the 40 medical malpractice attorneys answers it produced, Gemini recommended hiring a professional in 40% 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 17.5% 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 2.5%, averaging 278 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a medical malpractice attorneys buyer to a professional (82.5%) and Gemini the least (40%). ChatGPT produced the longest answers, at 522 words on average. No model named a specific provider in more than 0% of answers.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 20.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a medical malpractice attorneys buyer happens to ask matters most:
- Asks a clarifying question: from 2.5% (Gemini) to 80% (ChatGPT) — a 78-point spread.
- Recommends hiring a professional: from 40% (Gemini) to 82.5% (ChatGPT) — a 43-point spread.
- Mentions local proximity: from 10% (Gemini) to 45% (ChatGPT) — a 35-point spread.
- Gives price or cost information: from 15% (ChatGPT) to 40% (Claude) — a 25-point spread.
- Tells the buyer to verify credentials: from 2.5% (Gemini) to 17.5% (ChatGPT) — a 15-point spread.
The widest single gap — asks a clarifying question, 78 points — means a medical malpractice attorneys 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 medical malpractice attorneys market.
Where they agree
The points of near-consensus in Medical Malpractice Attorneys.
On other behaviors the three models move almost in lockstep — the points of near-consensus for medical malpractice attorneys, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Warns about red flags or scams: 10%–15% across all three (a 5-point spread).
- Suggests a DIY approach first: 2.5%–10% across all three (a 8-point spread).
- Gives selection criteria: 17.5%–27.5% 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 100% of questions) and least consistently on "asks a clarifying question" (15%).
Every behavior, measured
All twelve coded behaviors for Medical Malpractice Attorneys, averaged across the three models.
The behaviors AI models reproduce most often for medical malpractice attorneys are recommends hiring a professional (68.3% on average), asks a clarifying question (51.7%) and mentions local proximity (25%); the rarest are names a specific provider (0%), recommends multiple quotes (5.8%) and suggests a DIY approach first (5.8%). 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: 68.3% on average (ChatGPT 82.5%, Claude 82.5%, Gemini 40%) — a 43-point spread.
- Asks a clarifying question: 51.7% on average (ChatGPT 80%, Claude 72.5%, Gemini 2.5%) — a 78-point spread.
- Mentions local proximity: 25% on average (ChatGPT 45%, Claude 20%, Gemini 10%) — a 35-point spread.
- Gives price or cost information: 24.2% on average (ChatGPT 15%, Claude 40%, Gemini 17.5%) — a 25-point spread.
- Gives selection criteria: 23.3% on average (ChatGPT 25%, Claude 27.5%, Gemini 17.5%) — a 10-point spread.
- Warns about red flags or scams: 12.5% on average (ChatGPT 12.5%, Claude 15%, Gemini 10%) — a 5-point spread.
- Tells the buyer to verify credentials: 11.7% on average (ChatGPT 17.5%, Claude 15%, Gemini 2.5%) — a 15-point spread.
- Mentions case studies or portfolio: 10% on average (ChatGPT 12.5%, Claude 15%, Gemini 2.5%) — a 13-point spread.
- Tells the buyer to check reviews: 9.2% on average (ChatGPT 15%, Claude 10%, Gemini 2.5%) — a 13-point spread.
- Suggests a DIY approach first: 5.8% on average (ChatGPT 10%, Claude 5%, Gemini 2.5%) — a 8-point spread.
- Recommends multiple quotes: 5.8% on average (ChatGPT 12.5%, Claude 5%, Gemini 0%) — a 13-point spread.
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the medical malpractice attorneys buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the medical malpractice attorneys buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 9.2% of answers on average. Verifying credentials or certifications appeared in 11.7%. Warning about red flags or scams appeared in 12.5%.
On structuring the decision, a selection-criteria checklist showed up in 23.3% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for medical malpractice attorneys is "recommends multiple quotes" at 5.8% 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 Medical Malpractice Attorneys providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 medical malpractice attorneys answers, a specific provider was named in 0% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for medical malpractice attorneys: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Medical Malpractice Attorneys questions cover.
The 40 questions behind every percentage on this page were drawn from real medical malpractice attorneys (legal 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 medical malpractice attorneys 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 medical malpractice attorneys 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 →