Original research · 2026-07 edition

AI SEO Statistics: Medtech (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

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The question bank

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

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

What are the best remote patient monitoring services for elderly parents with heart conditions?
Is it cheaper to buy a home sleep study kit or go to a specialized clinic for a diagnosis?
How do I know if a telehealth company is actually HIPAA compliant before I share my data?
What should I look for in a provider that offers AI-driven diagnostic imaging services?
Are there any mobile phlebotomy services that will come to my office for blood work?
How much does a private MRI scan cost if I want to pay out-of-pocket without a referral?
What's the difference between a medical-grade wearable and a consumer fitness tracker for tracking arrhythmias?
I need a second opinion on my pathology report from a digital health service; how does that process work?
Show all 40 questions
What are the red flags to watch out for when vetting online prescription delivery services?
Can I rent a hospital-grade ventilator for home use, or do I have to hire a respiratory therapist too?
How do I verify the credentials of a remote radiologist reviewing my scans from another state?
Are there subscription-based primary care services that include all my annual diagnostic testing?
What is the average turnaround time for a direct-to-consumer DNA health risk assessment?
I'm looking for a local clinic that uses the latest non-invasive glucose monitoring technology for patients.
Is it worth paying extra for a 3D mammogram service over a standard 2D screening?
How do I compare the accuracy rates of different at-home colon cancer screening tests?
What questions should I ask a medical billing service before hiring them for my small private practice?
Are there any emergency dental services in my area that use 3D printing for same-day crowns?
How do I find a reputable provider for remote chronic disease management that integrates with my current doctor?
What are the pros and cons of using a virtual physical therapy service versus going to a local gym?
Does insurance usually cover remote cardiac monitoring services if I have a history of palpitations?
I need a portable oxygen concentrator for travel; should I buy one or use a long-term rental service?
What specific cybersecurity certifications should a medical data storage company have?
How do I find a lab that offers walk-in allergy testing without a three-week wait for an appointment?
What’s the price range for a private concierge doctor service that provides 24/7 home visits?
Are there any risks to using a third-party app to sync all my medical records from different hospitals?
How can I tell if a medical device repair service is certified by the original manufacturer?
I need a fast turnaround on a biopsy; which private labs are known for the quickest pathology results?
What is the functional difference between a retail health clinic and an urgent care center for minor stitches?
How do I evaluate the data security of a remote patient monitoring platform for my medical clinic?
Are there services that provide in-home ultrasound for high-risk pregnancies so I don't have to travel?
What should I expect to pay for a comprehensive executive health screening at a private facility?
Is it better to hire a local medical courier or use a national shipping service for sensitive lab samples?
How do I switch from a traditional pharmacy to a digital-first medication management service safely?
What are the signs that a telehealth provider is cutting corners on patient safety or diagnostic quality?
Can I get a professional-grade EKG done at a local pharmacy or do I need a specialist appointment?
How do I find a specialist surgeon who specifically uses robotic-assisted technology for knee replacements?
What’s the most cost-effective way to get regular blood sugar monitoring for a non-diabetic interested in longevity?
Are there any mobile clinics that provide employee vaccinations for large corporate offices on-site?
How do I vet a company that provides AI-powered symptom checkers for integration into my hospital's website?

Model by model

25-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 medtech buyers.

Behavior rates across 40 medtech buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional75%58%55%63%
Suggests DIY first15%8%13%83%
Names specific providers23%38%48%63%
Gives price or cost info20%18%23%88%
Tells to check reviews10%18%0%78%
Tells to verify credentials40%35%23%50%
Mentions case studies / portfolio5%5%5%90%
Mentions local proximity50%45%30%50%
Gives selection criteria68%70%38%15%
Warns about red flags18%20%23%70%
Asks a clarifying question53%68%3%18%
Recommends multiple quotes8%10%0%88%

By model

How each assistant handled Medtech questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same medtech questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 75% (ChatGPT) down to 55% (Gemini), a 20-point gap on an identical question set.

Across the 40 medtech answers it produced, ChatGPT recommended hiring a professional in 75% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 22.5% of answers (about 1.2 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 52.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 40%, averaging 569 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 50%; a selection-criteria checklist appeared in 67.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 medtech answers it produced, Claude recommended hiring a professional in 57.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 37.5% of answers (about 1.2 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 35%, averaging 302 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 45%; a selection-criteria checklist appeared in 70% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 medtech answers it produced, Gemini recommended hiring a professional in 55% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 47.5% of answers (about 1.8 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 22.5%, averaging 253 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a medtech buyer to a professional (75%) and Gemini the least (55%). ChatGPT produced the longest answers, at 569 words on average. Specific providers were named most often by Gemini (47.5%) — even there, roughly one answer in 2 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a medtech buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 67.5% (Claude) — a 65-point spread.
  • Gives selection criteria: from 37.5% (Gemini) to 70% (Claude) — a 33-point spread.
  • Names a specific provider: from 22.5% (ChatGPT) to 47.5% (Gemini) — a 25-point spread.
  • Recommends hiring a professional: from 55% (Gemini) to 75% (ChatGPT) — a 20-point spread.
  • Mentions local proximity: from 30% (Gemini) to 50% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 65 points — means a medtech 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 medtech market.

Where they agree

The points of near-consensus in Medtech.

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

  • Mentions case studies or portfolio: 5% across all three models.
  • Gives price or cost information: 17.5%–22.5% across all three (a 5-point spread).
  • Warns about red flags or scams: 17.5%–22.5% across all three (a 5-point spread).
  • Suggests a DIY approach first: 7.5%–15% across all three (a 8-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 90% of questions) and least consistently on "gives selection criteria" (15%).

Every behavior, measured

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

The behaviors AI models reproduce most often for medtech are recommends hiring a professional (62.5% on average), gives selection criteria (58.3%) and mentions local proximity (41.7%); the rarest are mentions case studies or portfolio (5%), recommends multiple quotes (5.8%) and tells the buyer to check reviews (9.2%). 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: 62.5% on average (ChatGPT 75%, Claude 57.5%, Gemini 55%) — a 20-point spread.
  • Gives selection criteria: 58.3% on average (ChatGPT 67.5%, Claude 70%, Gemini 37.5%) — a 33-point spread.
  • Mentions local proximity: 41.7% on average (ChatGPT 50%, Claude 45%, Gemini 30%) — a 20-point spread.
  • Asks a clarifying question: 40.8% on average (ChatGPT 52.5%, Claude 67.5%, Gemini 2.5%) — a 65-point spread.
  • Names a specific provider: 35.8% on average (ChatGPT 22.5%, Claude 37.5%, Gemini 47.5%) — a 25-point spread.
  • Tells the buyer to verify credentials: 32.5% on average (ChatGPT 40%, Claude 35%, Gemini 22.5%) — a 18-point spread.
  • Gives price or cost information: 20% on average (ChatGPT 20%, Claude 17.5%, Gemini 22.5%) — a 5-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 17.5%, Claude 20%, Gemini 22.5%) — a 5-point spread.
  • Suggests a DIY approach first: 11.7% on average (ChatGPT 15%, Claude 7.5%, Gemini 12.5%) — a 8-point spread.
  • Tells the buyer to check reviews: 9.2% on average (ChatGPT 10%, Claude 17.5%, Gemini 0%) — a 18-point spread.
  • Recommends multiple quotes: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Mentions case studies or portfolio: 5% on average (ChatGPT 5%, Claude 5%, Gemini 5%).

Trust signals

How well the models protect the medtech buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the medtech 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 32.5%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 58.3% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for medtech 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 Medtech providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 medtech answers, a specific provider was named in 35.8% of responses on average — roughly 1.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for medtech: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Medtech questions cover.

The 40 questions behind every percentage on this page were drawn from real medtech (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 medtech 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 medtech 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 →