Category
Principles of LLM optimization (Large Language Model Optimization): document structures that are easy to cite, entity design, E-E-A-T, and the practice of stating sources.
Related entities: LLMO / LLM最適化 / 引用最適化 / E-E-A-T
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."
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.
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.
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.