How do LASIK patients find surgeons using AI in 2026?

LASIK patients in 2026 increasingly begin their surgeon research by asking ChatGPT or Perplexity questions like 'What is the best LASIK surgeon in [city]?', 'Is LASIK permanent?', or 'What is the difference between LASIK and SMILE?' The AI synthesises responses from indexed web content, schema markup, and Google Business Profile data. Ophthalmology practices that appear in these responses have procedure-specific content with clinical depth, Ophthalmologist schema, physician credential markup, and AI-readable FAQs covering pre-consultation questions.

What schema markup does an ophthalmology practice need for AI search?

An ophthalmology practice needs MedicalBusiness schema with medicalSpecialty set to Ophthalmology, MedicalProcedure schema for LASIK, SMILE, PRK, cataract surgery, and other offered procedures, Person schema for each surgeon with medical school, fellowship, and board certification data, FAQPage schema on patient Q&A content, and Speakable schema on procedure descriptions and FAQ answers for voice search delivery.

Why is my ophthalmology practice not showing up in ChatGPT recommendations?

Most ophthalmology practices are absent from ChatGPT recommendations because they lack AI crawler access (blocked by robots.txt or Cloudflare WAF), have no llms.txt file, use generic medical website content that AI systems cannot differentiate between practices, and have GBPs with incorrect primary categories. An ophthalmology practice using 'Optometrist' as its primary GBP category instead of 'Ophthalmologist' or 'LASIK Surgeon' will miss high-intent procedure queries entirely.

A Patient Asked ChatGPT for the Best LASIK Surgeon in Their City. Was Your Practice in the Answer?

LASIK is one of the most researched elective procedures in medicine. Patients spend weeks comparing surgeons, reading outcome data, and asking detailed questions before committing to a consultation. In 2026, most of that research begins not with a Google search — but with a conversation. With ChatGPT. With Perplexity. With AI. And for most ophthalmology practices, that conversation is happening without them.

The LASIK Patient Research Journey Has Moved to AI

LASIK patients are not impulsive buyers. The average patient considering LASIK spends three to eight weeks researching before booking a consultation. They ask detailed questions: Is LASIK permanent? What is the difference between LASIK and SMILE? Am I a candidate? What are the risks? Who are the most experienced surgeons in my city? In 2021 and 2022, most of this research happened on Google and YouTube. In 2026, a substantial and growing share happens in AI chatbot conversations — because AI chatbots answer follow-up questions without requiring the patient to click through multiple websites and synthesise information themselves.

When a patient asks ChatGPT "who are the best LASIK surgeons in Dallas?", ChatGPT retrieves content from indexed web sources, cross-references its knowledge of practitioners in the area, and synthesises a response. The surgeons who appear in that response are not necessarily the most clinically experienced. They are the ones whose digital infrastructure makes them legible and authoritative to AI systems — those with procedure-specific pages that have clinical depth, physician credential markup, AI-readable FAQ content, and structured GBP data that identifies them as LASIK surgeons rather than generic eye doctors.

The ophthalmology practices missing from these responses have websites with generic medical content, GBPs with incorrect categories, no schema markup, and no guidance files for AI crawlers. Their clinical quality may be excellent. Their AI visibility is zero. And they have no idea the inquiry ever happened.

The Specific Visibility Problem in Ophthalmology

Ophthalmology has a particular AI visibility challenge that makes it more acute than many other specialties. The technical complexity of the field — the range of procedures (LASIK, SMILE, PRK, ICL, cataract surgery with premium IOLs, retinal procedures), the clinical nuance around candidacy, and the high stakes of vision correction — means that patients ask AI systems extremely specific questions. "Is SMILE better than LASIK for thin corneas?" is a real patient query. The AI needs a practice whose content addresses that specific question with clinical accuracy to produce a reliable citation. Most ophthalmology websites, built as marketing materials rather than clinical resources, cannot satisfy that specificity.

There is also a widespread GBP category problem in ophthalmology. Many practices use "Optometrist" as their primary Google Business Profile category when their primary revenue comes from surgical ophthalmology. Others use "Eye Care Center" — a generic category that AI systems cannot map to specific high-value procedure queries like "LASIK surgeon near me." The GBP primary category is one of the most significant signals in AI Mode's local recommendation logic. Using the wrong one systematically excludes the practice from the highest-intent AI search queries.

The physician credential problem is equally significant. When a patient asks ChatGPT about the best cataract surgeon in their city, the AI weights physician authority as a quality signal — fellowship training, board certifications, subspecialty credentials. If that information exists only as unstructured text buried in a bio page ("Dr. Chen completed her fellowship at Johns Hopkins..."), the AI must interpret it with no certainty. If it is structured as Person schema with explicit alumniOf, memberOf, and hasCredential fields, the AI reads it as verified, structured authority. The difference in citation probability is substantial.

What It Takes to Be in That ChatGPT Answer

The infrastructure required to appear in AI recommendations for LASIK and other high-value ophthalmology procedures is specific and buildable. It does not require a website rebuild. It requires an infrastructure layer on top of an existing website, plus targeted content production and GBP correction.

The schema layer starts with MedicalBusiness schema identifying the practice as an ophthalmology entity with the correct medicalSpecialty property. Each offered procedure — LASIK, SMILE, PRK, ICL, cataract surgery, premium IOL — gets its own MedicalProcedure schema block with procedureType, bodyLocation, and indication fields. Each surgeon gets a Person schema entry with medical school, residency, fellowship, and board certification structured as machine-readable attributes. FAQPage schema goes on all patient Q&A content, Speakable schema tags the procedure descriptions and FAQ answers for voice delivery. The BreadcrumbList schema connects each procedure page to the practice's content hierarchy.

The content layer requires procedure pages written to current E-E-A-T standards — not marketing copy, but clinical resource pages that answer the specific questions patients ask AI systems before their first consultation. "Is LASIK permanent?" "Am I a candidate if I have astigmatism?" "What is the recovery timeline for SMILE?" These are the questions AI chatbots receive, and a practice whose content directly and accurately answers them becomes the cited source.

The GBP correction starts with primary category. For a surgical ophthalmology practice, the primary category should be "Ophthalmologist" — not "Optometrist," not "Eye Care Center." Secondary categories should reflect the highest-revenue procedure lines: "LASIK Surgeon," "Cataract Surgery," "Refractive Surgery." Services should include individual entries for each procedure with procedure-specific descriptions. These GBP changes, combined with the schema and content layer, make the practice recognisable to AI Mode as a specific type of provider for specific procedures in a specific geography.

The Window Is Open in Ophthalmology

Ophthalmology is a newer market for AI visibility infrastructure than medical aesthetics or dentistry. The competitive window — the period before most competitors have built AI-legible infrastructure — is wider here. A practice that builds the right stack now in a market like Dallas, Houston, Phoenix, or Nashville has a meaningful probability of becoming the default AI-recommended LASIK provider in its geography before any competitor catches up.

The average agentic readiness score across the ophthalmology practices Iris by AdChoreo has assessed is lower than the med spa average of 47 out of 100. Ophthalmology websites tend to be built on legacy medical content management systems with limited schema support and no AI-specific infrastructure. The gap is larger — and so is the upside for first movers.

If you want to know exactly where your practice stands, the free agentic readiness audit takes 60 seconds. It scores your practice across all six AI visibility dimensions — GBP completeness, on-page AI readability, schema coverage, citation consistency, root-level AI files, and active AI surfacing — and shows you exactly which gaps to close first. For ophthalmology practices, the GBP category finding and the schema gap are almost always the most actionable. Both can be addressed in the first 30 days of an engagement.