Highlights
- The dataset: Over 120,000 AI source citations underpin this analysis, produced by prompts specific to the medical device industry. The AI responses from four AI models span 12 countries over the course of Q2 2026.
- 89% of top sources differ between any two models: The most divergent pairs barely agree at all, overlapping just 5%.
- 60% of Microsoft Copilot's citations are from commercial domains: Vendor and procurement directories, it reads like a catalog.
- 90% of Microsoft Copilot's Dutch citations are local .nl domains: In regulated markets it behaves like a local procurement search.
Which sources should you target to get cited as a medical devices brand?
The single most-cited domain is different for every model, and none of them is a device manufacturer. Grok and Microsoft Copilot lean on B2B lead-generation and procurement directories; ChatGPT reaches for reference and community content; Google AI Overview cites its own search surface most of all.
| Model | #1 | #2 | #3 | #4 | #5 |
|---|---|---|---|---|---|
| ChatGPT | en.wikipedia.org (4.8%) | reddit.com (4.3%) | pharmchoices.com (3.5%) | f6s.com (1.8%) | de.wikipedia.org (1.4%) |
| Microsoft Copilot | pharmchoices.com (5.7%) | aeroleads.com (4.5%) | f6s.com (3.0%) | vegro.nl (2.2%) | zoominfo.com (2.1%) |
| Grok | f6s.com (3.2%) | biopharmguy.com (3.0%) | tracxn.com (2.7%) | app.biopharmiq.com (2.6%) | glassdoor.com (1.7%) |
| Google AI Overview | google.com (7.2%) | instagram.com (2.8%) | tracxn.com (1.6%) | seamless.ai (1.1%) | promedon.com (1.0%) |
The character of each list is distinct: Microsoft Copilot reads like a medical-procurement catalog, Grok like a startup-and-hiring database, ChatGPT like an encyclopedia-plus-forum digest, and Google AI Overview like a search-and-social blend. Manufacturer domains (stryker.com, molnlycke.com, jnjmedtech.com) surface only deeper in the lists, the AI engines cite intermediaries about medical-device companies far more than the companies' own sites. Grok's wide net means many chances to be cited per answer, but each citation carries less weight; ChatGPT's narrow net means fewer slots and higher stakes for each one.
How have AI source rankings changed over time in the medical device industry?
We split the observation window at its midpoint (early May 2026) and compared each top domain's share of citations in each half, ranking by share within each half rather than raw counts.
The B2B directories, startup databases, and social/hiring sites that led the first half slide in the second: f6s.com (#1 to #7), biopharmguy.com (#2 to #11), tracxn.com (#3 to #10), and glassdoor.com, linkedin.com, crunchbase.com, labiotech.eu, and seedtable.com all fall out of the top-20 entirely. In their place, google.com, en.wikipedia.org, and reddit.com are all new to the top-20. Over the window the category's citation profile shifts toward reference, search, and community content.
What type of content do AI models cite for medical devices?
The models differ not just on which domains but on what type of content they trust. We classified every cited domain by category and measured the mix per model. Commercial content dominates every model, but the balance of the remainder differs sharply.
For a device maker, the read is direct: Microsoft Copilot and Google AI Overview reward presence on commercial procurement and vendor directories most heavily; Grok additionally values user-generated content (reviews, forums, hiring sites); and ChatGPT is the only model that meaningfully rewards reference content, Wikipedia presence and well-sourced encyclopedic coverage matter far more for ChatGPT than for any other engine, at 12.6%, far above the others (Copilot 5.4%, Grok 2.0%, Google AI Overview 0.9%).
Do AI models cite local-language content for medical devices?
Across the eight non-English markets we measured, 44.4% of citations point to a local-country domain, a higher localization rate than most global B2B categories. Medical-device procurement is often nationally regulated and served by in-country distributors, and the AI models reflect that: local suppliers, national directories, and regional distributors carry real weight.
The Netherlands and Germany are the most locally grounded, Dutch and German device buyers pull heavily on in-country distributors and directories. Spain is the outlier on the low end, leaning on global .com and pan-Spanish sources rather than .es domains, and the three Spanish-language markets diverge widely (Argentina 53.9% vs Spain 26.7%), so "Spanish" is not a single localization story.
Which AI model relies most on local sources for medical devices?
The same prompt, asked in the same language, produces very different localization behavior depending on the model. Microsoft Copilot and ChatGPT rely most on local sources aggressively overall; Grok reaches for global sources most often.
Microsoft Copilot cites local domains 90% of the time in the Dutch market and two-thirds of the time in German, it behaves almost like a local procurement search there. Grok, by contrast, defaults toward global sources, localizing least in French (26.9%) and Spanish (36.7%). Google AI Overview returned no French-market cell in this dataset, so its French figure is omitted. A brand targeting Dutch or German buyers gets the most local-source visibility through Microsoft Copilot.
Should medical device companies optimize for each AI model separately?
Yes, almost entirely. We built each model's top-20 most-cited domains and compared every pair: the share of domains two lists hold in common out of all the domains they name between them. The average overlap is 11.1%, well below what a shared "authoritative source" market would produce.
Only one domain, f6s.com, a startup-and-company directory, appears in all four models' top-20 lists. It is the closest thing the medical-device category has to a universally trusted source, and it is a directory, not a manufacturer or a medical publication. Everything else is contested. Google AI Overview and Grok genuinely draw from a more similar pool than any other pairing, but the headline holds across all six comparisons: overlap is uniformly low.
How many sources does each AI model cite per answer?
The four models differ enormously in how many sources they pull into a single answer. Grok is the most source-hungry by far, averaging 31.6 cited sources per medical-device response; ChatGPT is the most economical at 6.5, a 4.9x gap.
| Model | Mean cited sources/response | 95% range | Responses |
|---|---|---|---|
| Grok | 31.6 | [30.4, 32.8] | 2,378 |
| Google AI Overview | 9.9 | [9.7, 10.0] | 1,513 |
| Microsoft Copilot | 8.7 | [8.6, 8.9] | 3,492 |
| ChatGPT | 6.5 | [6.3, 6.7] | 1,280 |
For a device maker, source count is opportunity: Grok's wide net means many chances to be cited per answer, but each citation carries less weight; ChatGPT's narrow net means fewer slots and higher stakes for each one. Grok's volume also explains its outsized influence on the aggregate.
Context
This analysis draws from Temso's AI visibility monitoring platform, which tracks how brands appear in AI model responses across ChatGPT, Microsoft Copilot, xAI Grok, and Google AI Overview. The dataset covers medical-device-company prompts across twelve markets (US, UK, Canada, Australia, Germany, France, Italy, Netherlands, Sweden, Spain, Mexico, Argentina) and seven languages over roughly sixteen weeks (March–June 2026).
It measures what AI models cite, not the accuracy of any answer or the traffic value of a citation. The localization measure is a country-domain (ccTLD) proxy for local-language content, a German-language article on a .com domain counts as non-local here. These findings reflect citation behavior for medical-device prompts in the monitored markets, and may not generalize to all prompt types or other verticals.
Methodology
How we measured this
We analyzed over 120,000 cited source links extracted from 11,498 AI model responses to medical-device-company prompts, collected between 2026-03-10 and 2026-06-29 across twelve countries and seven languages. A "citation" is a single web source a model referenced in answering a prompt; we counted only sources flagged as actually cited, not merely retrieved. For each model we ranked domains by citation count, then measured pairwise overlap on each model's top-20 list using Jaccard similarity (shared ÷ combined unique domains); the most-overlapping pair differs significantly from the least (z = 2.68, p = 0.007). Domain categories were assigned from a global registry. Localization is measured by a country-code-domain proxy for the eight non-English markets, excluding English prompts, which sit overwhelmingly on generic domains, so those figures are directional and likely undercount local-language pages on global TLDs.
Temporal analysis split the window at its midpoint. Model coverage varied across the observation window, so temporal movements are reported as relative ranks within each half. The overall sample far exceeds recommended thresholds for reliable proportion estimates; per-model and per-language cells are all well-powered except where marked n/a.
Frequently asked questions
Do different AI models cite the same medical-device sources?
No, overwhelmingly not. Any two of the four models share on average just 11.1% of their top-20 domains, so about 89% of the sources one model trusts are absent from another's top tier. Only f6s.com, a company directory, appears across all four lists.
Which AI model cites the most sources per answer?
Grok, by a wide margin, about 32 sources per medical-device response, versus roughly 10 for Google AI Overview, 9 for Microsoft Copilot, and about 6 to 7 for ChatGPT. That is a 4.9x spread between the most and least source-hungry models.
Which single domain matters most in medical-device AI answers?
It depends on the model. ChatGPT leads with en.wikipedia.org; Microsoft Copilot with pharmchoices.com; Grok with f6s.com; Google AI Overview with its own google.com surface. f6s.com is the only domain trusted by all four, and none of the leaders is a device manufacturer.
What type of content gets cited most for medical devices?
Commercial content leads for every model, 38% to 60% of citations, but the mix differs. Microsoft Copilot and Google AI Overview crowd into commercial directories; Grok adds heavy user-generated content (17.9%); and ChatGPT is the only model that meaningfully cites reference content (12.6%, more than double any other model, with Copilot next at 5.4%).
Do AI models cite local-language content in non-English markets?
Yes, more than most B2B categories. Across eight non-English markets, about 44% of citations use a local-country domain. The Netherlands (59%) and Germany (55%) are the most local; Spain (27%) the least. Medical-device procurement is nationally regulated and distributor-driven, pushing the models toward in-country sources.
Which model is best for reaching non-English audiences?
Microsoft Copilot and ChatGPT rely most on local sources, about 53–54% of their citations in non-English prompts are local-domain, ahead of Google AI Overview (45%) and Grok (40%). A brand targeting Dutch or German buyers gets the most local-source visibility through Microsoft Copilot, which cites local .nl domains 90% of the time in the Netherlands.
Did the medical-device rankings change over the period studied?
Yes. Directory and startup-database domains (f6s.com, biopharmguy.com, tracxn.com, glassdoor.com) slid in the second half while reference and search sources rose, shifting the citation mix toward reference, search, and community content.
What should a medical-device company do with this?
Optimize per model, not in general. Winning Microsoft Copilot and Google AI Overview rewards procurement-directory and vendor-listing presence; winning Grok rewards startup-database, review, and hiring-site presence; winning ChatGPT requires strong reference (Wikipedia) and community coverage. Because the models overlap so little and cite intermediaries over manufacturer sites, an owned-site-only strategy leaves you invisible across most of the AI search surface.
