Practical knowledge for medical institutions to be correctly understood and referenced in AI search. We publish it structured around LLMO, GEO, SEO, MEO, and the Medical Advertising Guidelines.
We empirically tested the common claim that "if you write an FAQ, AI will cite it verbatim" across 30 questions × 5 AI search environments and 150 responses. Zero verbatim matches. And a more important fact: search itself often does not happen.
We expanded the machine-readability survey of 400 facilities in Osaka to the whole country, conducting a complete census of the machine readability of 7,643 aesthetic-related clinics. AI crawlers fully allowed 88.7%, llms.txt 9.1%, structured data 40.0%. And about one-tenth of registered URLs were unreachable.
We conducted a machine-readability survey of 400 facilities in Osaka Prefecture advertising cosmetic surgery, aesthetic dermatology, plastic surgery, or dermatology. AI crawlers fully allowed 89%, llms.txt present 10%, structured data 52%. The current state of medical institutions' "readiness."
A procedure for observing how your clinic is treated in AI search, without any special tools. How to frame the questions, which environments to try, how to keep records, and why you should not judge from a single result.
Generative engine optimization (GEO) has research grounded in actual measurement. We organize the findings of two representative studies and consider how they can be translated into information delivery for medical institutions.
We ran a machine-readability survey of 67 domestic sites that explain AI search optimization. 93% allow all AI crawlers; 27% deploy llms.txt. The actual state of the "preparedness" on the explaining side, and a comparison with medical institutions.
A site right after launch does not appear in AI's recommendation lists. The first installment of a record that observes, over time, the distance between readability and recommendation, using this site itself as the subject.
Reflection in AI search does not necessarily stabilize right after publication. Rather than judging immediately after publishing, we organize when to observe and when to suspect a technical problem.
When patients ask an AI about medical institutions, how are clinics and doctors compared? This article organizes the information you want to have in order—across your website, doctor profiles, reviews, case examples, and FAQs—so that AI conveys your clinic's information correctly.
Starting from the question "If I write good articles, will AI reference me?", this piece organizes how—beyond content quality—trust signals such as external reference information, SEO assets, reviews, backlinks, branded search, and brand recognition can influence how medical institutions are treated in AI search.
If you ask an AI once, you can get some sense of how your clinic appears and where it can improve. But AI answers change depending on the wording of the question, the model, the flow of the conversation, and the timing. Deciding your strategy from a single answer is risky. This article organizes the premises for continuously observing how medical institutions are treated in AI search.
Organizes the intent of the Ministry of Health, Labour and Welfare's "Medical Advertising Guidelines" and the items that most often become points of contention in the web expressions of self-pay clinics (conditional exemption, testimonials, before/after photos, and reviews).
A concrete approach to applying Google's E-E-A-T evaluation framework to the medical field. It explains the steps for demonstrating Experience/Expertise/Authoritativeness/Trustworthiness on a clinic website.
A primer on GEO and its relationship to LLMO and SEO. We explain the document design that helps medical institutions appear in the answers of generative AI.
How medical-institution information is handled in Google AI Overview / AI Mode, ChatGPT, Claude, and Perplexity. We organize the differences in perspective to the extent needed for management decisions.
This article organizes how information about self-pay (private) care clinics is treated by ChatGPT Search / Perplexity / Google AI Overviews, and explains how to craft documents and align naming so as to be cited.
As AI search spreads, we organize from a practical standpoint how Google Business Profile and reviews are referenced, and how medical institutions can prepare.