Original research
The State of AI Visibility for Australian Clinics 2026
Two studies. We ran 25 patient-style searches across five clinical sectors and five capital cities, then audited 50 real Australian clinic homepages for the signals answer engines rely on. Directories are winning, and the reason is fixable.
Published 13 July 2026 · Free to read and cite with attribution
Key findings
- 64%
- of AI-style clinic searches were dominated by directories
- 56%
- carry the schema type that actually matters for AI answers
- 94%
- ship some structured data, which is why audits look reassuring
- 10%
- have FAQ schema, the single biggest missed lever
- 28%
- publish a real llms.txt file
- 4.04
- clinic websites nameable per search, on average
Why this matters
Answer engines now sit between patients and clinics. When someone asks ChatGPT, Perplexity or Google AI Overviews for a good GP, dentist, psychologist, physiotherapist or skin clinic in their city, the reply is not ten blue links. It is a short, confident shortlist of names. Whoever is named wins the first, and often the only, consideration a patient gives.
Our first study shows those names are heavily skewed towards directories and aggregators rather than clinic websites. In almost two thirds of the searches we ran, the top of the answer was owned by booking platforms, review aggregators, forum threads and editorial top ten listicles. Clinics were present. They were rarely what the answer leaned on.
Our second study explains part of why, and the encouraging part is that the strongest signals are also the most neglected. The room to leapfrog is real and largely uncontested.
Part A: who wins when patients ask AI for a clinic
We ran 25 patient-style searches: five sectors across Sydney, Melbourne, Brisbane, Perth and Adelaide. Every single query, all 25, surfaced at least one specific clinic website in the top eight. So clinics are not invisible. The problem is prominence.
In 64% of queries, three or more of the top five results were directories, aggregators, forums or listicles rather than clinic websites. On average only 4.04 distinct clinic websites were nameable per query.
| Sector | Dominated searches | Share |
|---|---|---|
| General practice | 4 of 5 | 80% |
| Dental | 4 of 5 | 80% |
| Psychology | 4 of 5 | 80% |
| Physiotherapy | 3 of 5 | 60% |
| Cosmetic and skin | 1 of 5 | 20% |
The split is instructive. Sectors with mature booking platforms and association directories, such as general practice, dental and psychology, are hardest for a clinic to own directly, because the aggregators are strong and well structured. Cosmetic and skin, where clinics invest more in their own websites, was the one sector where clinic sites rather than directories tended to win.
Part B: are clinics ready for AI search
We audited 50 real Australian clinic homepages, ten per sector, checking the server-rendered HTML for five signals answer engines rely on.
| Signal | Present | Share |
|---|---|---|
| Any JSON-LD structured data | 47 of 50 | 94% |
| LocalBusiness or medical schema type | 28 of 50 | 56% |
| FAQPage schema | 5 of 50 | 10% |
| A real llms.txt file | 14 of 50 | 28% |
| Non-empty meta description | 48 of 50 | 96% |
We classed a homepage as AEO-ready only when it had JSON-LD, a LocalBusiness or medical-specific type, and a meta description together. On that stricter test, 28 of 50 qualified. Readiness was uneven, and the leaders were not the obvious ones.
| Sector | AEO-ready |
|---|---|
| Cosmetic and skin | 70% |
| Physiotherapy | 70% |
| Dental | 60% |
| General practice | 40% |
| Psychology | 40% |
The schema gap
This is the sharpest finding. Almost every clinic ships some structured data, 94% of them, so on a surface audit they all look covered. Only 56% ship a LocalBusiness or medical-specific type. The rest ship generic markup, most commonly a bare Organisation block, which tells an engine a website exists but almost nothing about what kind of clinic it is, where it operates or what it treats.
So the real readiness number is not the reassuring 94%. It is 56%, and the difference between them is pure recoverable upside.
FAQ schema is the biggest untapped lever of all. Only 10% of clinics use it, yet it is the most direct way to hand an answer engine ready-made question and answer pairs it can quote verbatim. Ninety per cent of clinics are leaving that on the table.
What to do about it
Five fixes, in order of impact
None of these require a rebuild, and most are one-off changes that keep paying off.
- 01
Ship the correct schema type
Replace generic Organisation markup with a LocalBusiness or medical-specific type naming your services, practitioners, location and hours. This is the change that moves you from the 94% who look ready to the 56% who are.
- 02
Add FAQ schema
Turn the questions patients actually ask into structured question and answer pairs on your key pages. With only 10% of clinics doing this, it is the fastest way to stand out.
- 03
Make your listings agree
Answer engines cross-check name, address and phone across sources. Consistent listings turn the directories currently outranking you into signals that reinforce you.
- 04
Publish a genuine llms.txt
A clean, plain-language summary of who you are and what you offer. Only 28% of clinics have one, and many of those are auto-generated stubs rather than deliberate files.
- 05
Write pages that answer directly
Lead with the question, give a clear 40 to 60 word answer, then expand. Directly answerable content is what an engine can lift into its reply.
Methodology
Part A measured AI recommendation visibility using 25 patient-style searches: five sectors (general practice, dental, psychology, physiotherapy, and cosmetic and skin) across five Australian capital cities. Each search used a natural "best sector in city" query with an Australian location set through a web-search proxy, recording the top eight results. A search counted as directory-dominated when three or more of its top five results were directories, aggregators, forums or listicles rather than clinic websites.
Part B audited 50 real Australian clinic homepages, ten per sector. For each we fetched the server-rendered HTML and checked five signals: any JSON-LD, a LocalBusiness or medical-specific type, FAQPage schema, a real llms.txt at the site root, and a non-empty meta description. Both studies were run in 2026 and report sector and city level aggregates only. No individual clinic is identified.
Limitations
We want this to be useful, so we are direct about what it cannot claim.
- The sample is purposive, not random. Clinics were drawn from those that already rank for their sector and city, so the set skews towards established, marketing-active practices. True population readiness across all Australian clinics is very likely lower than these figures.
- Web search was a proxy for AI answers. A reasonable stand-in for what answer engines surface, but not a literal capture of a ChatGPT or Perplexity response, which varies by user, session and time.
- Schema detection was server-rendered HTML only. Structured data injected later by JavaScript would be undercounted, so the schema figures are conservative.
- Sample sizes are small per cell. Five searches and ten homepages per sector support direction, not precision. Treat the sector splits as indicative.
The full report
Take the report with you.
The findings above are free to read and cite. The full report is a formatted PDF with every figure, the method and the checklist we use on client sites, ready to share with your practice manager or your board.
Leave your details and it downloads on the next page.
Journalists and researchers who want the raw per-query data, email contact@maxconnex.ai and we will send it the same day.
Answers
About this research
Which is the weakest AEO signal for Australian clinics?
FAQ schema. Only 10% of the 50 clinic homepages we audited included FAQ structured data, and only 28% published a real llms.txt file. FAQ schema is the single biggest untapped lever, because it hands an answer engine ready-made question and answer pairs it can quote verbatim.
Are Australian clinics invisible to AI?
No, and that is the more useful finding. Every one of the 25 patient-style searches we ran surfaced at least one specific clinic website in the top eight. The problem is not invisibility, it is prominence: in 64% of searches, three or more of the top five results were directories, aggregators or listicles rather than clinic websites.
Which sector is best prepared?
Cosmetic, skin and physiotherapy clinics led on readiness at 70%, ahead of dental at 60% and both general practice and psychology at 40%. Cosmetic and skin was also the one vertical where clinic sites rather than directories tended to win the search, which is a preview of what happens once clinics structure their own presence properly.
How reliable are these numbers?
Directionally reliable, with stated limits. The sample is purposive rather than random, drawn from clinics that already rank, so true population readiness is very likely lower than these figures. Web search was used as a proxy for AI answers rather than a literal capture of a ChatGPT response. Schema detection was server-rendered HTML only, so the structured data figures are conservative.
Can I use this research?
Yes, with attribution to MaxConnex and a link to this page. If you want the underlying dataset, ask and we will send it.
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