--- title: "AI Search Optimization for Healthcare: The 2026 Medical LLMO Playbook" description: "AI search optimization for healthcare in 2026: how YMYL, clinician E-E-A-T, MedicalOrganization and Physician schema, PubMed and WHO citations, EU AI Act high-risk classification, and HIPAA/GDPR disclaimers shape medical AI visibility." lang: en json-ld: | [ { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://alicelabs.ai/#organization", "name": "Alice Labs", "alternateName": [ "Alice Labs AB", "AliceLabs" ], "legalName": "Alice Labs AB", "identifier": "559443-5470", "foundingLocation": { "@type": "Place", "name": "Stockholm, Sweden" }, "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "@id": "https://alicelabs.ai/#logo", "url": "https://alicelabs.ai/images/alice-logo.png", "contentUrl": "https://alicelabs.ai/images/alice-logo.png", "width": 2000, "height": 2027, "caption": "Alice Labs" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "description": "Alice Labs är en svensk AI-byrå som hjälper företag implementera AI - från strategi till skalning.", "slogan": "From AI strategy to measurable results.", "foundingDate": "2023", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressCountry": "SE" }, "contactPoint": [ { "@type": "ContactPoint", "contactType": "customer service", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "areaServed": [ "SE", "EU" ], "availableLanguage": [ "Swedish", "English" ] } ], "areaServed": [ { "@type": "Country", "name": "Sweden" }, { "@type": "Place", "name": "Europe" } ], "knowsAbout": [ "AI strategy", "AI implementation", "AI agents", "AI automation", "Generative AI", "AI governance", "AI training", "Machine learning", "Large language models", "RAG", "AI consulting", "Digital transformation", "AI search optimization", "LLMO", "AI for enterprise" ], "founder": [ { "@id": "https://alicelabs.ai/#linus" }, { "@id": "https://alicelabs.ai/#eric" } ], "sameAs": [ "https://www.linkedin.com/company/alicelabsai", "https://www.trustpilot.com/review/alicelabs.ai", "https://www.wikidata.org/wiki/Q140369570" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "givenName": "Linus", "familyName": "Ingemarsson", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. 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"https://alicelabs.ai/en/insights/ai-search-optimization-healthcare", "datePublished": "2026-05-06", "dateModified": "2026-09-13", "expires": "2026-12-12", "author": { "@id": "https://alicelabs.ai/#linus" }, "reviewedBy": { "@id": "https://alicelabs.ai/#eric" }, "dateReviewed": "2026-09-13", "publisher": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "url": "https://alicelabs.ai/images/alice-logo.png" } }, "image": { "@type": "ImageObject", "@id": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#hero-image", "url": "https://alicelabs.ai/images/og/og-home.jpg", "contentUrl": "https://alicelabs.ai/images/og/og-home.jpg", "width": 1600, "height": 900, "caption": "AI Search Optimization for Healthcare: The 2026 Medical LLMO Playbook", "creator": { "@id": "https://alicelabs.ai/#organization" }, "representativeOfPage": true, "license": "https://alicelabs.ai/terms" }, "mainEntityOfPage": { "@type": 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"https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#citation-hierarchy-medical" }, { "@type": "Thing", "name": "EU AI Act Annex III: Healthcare AI as High-Risk", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#eu-ai-act-healthcare" }, { "@type": "Thing", "name": "Compliance as Citation Signal: HIPAA, GDPR, and Medical Disclaimers", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#compliance-disclaimers" } ], "mentions": [ { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, { "@type": "Organization", "name": "Google", "url": "https://google.com" }, { "@type": "Organization", "name": "European Union", "url": "https://europa.eu" }, { "@type": "Product", "name": "ChatGPT", "url": "https://chatgpt.com" }, { "@type": "Product", "name": "Claude", "url": "https://claude.ai" }, { "@type": "Product", "name": "Google Gemini", "url": "https://gemini.google.com" }, { "@type": "Person", "name": "Eric Lundberg", "url": "https://linkedin.com/in/eric-lundberg-3530451bb" }, { "@type": "Organization", "name": "Linus Ingemarsson" }, { "@type": "Organization", "name": "Aggarwal et al." }, { "@type": "Organization", "name": "PubMed (US National Library of Medicine" } ], "hasPart": [ { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Healthcare Sits at the Top of the YMYL Bar", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#healthcare-and-ymyl-bar", "description": "Google classifies medical and health content as the highest tier of YMYL — the Search Quality Rater Guidelines explicitly call out medical information as content that can directly affect a person's health and safety. ChatGPT, Claude, and Perplexity inherit and intensify that conservatism, defaulting to PubMed, WHO, NIH, and CDC over hospital marketing pages." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "E-E-A-T for Healthcare: MD, PhD, RN, Board Certifications", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#e-e-a-t-for-healthcare", "description": "E-E-A-T for healthcare demands four signals LLMs can verify: real clinicians with MD, PhD, RN, or board certifications, peer-reviewed citations on every load-bearing claim, named clinical experience, and transparent disclaimers about medical advice and the physician-patient relationship." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Schema.org Medical Types for Healthcare LLMO", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#schema-medical-types", "description": "Healthcare sites should ship six Schema.org medical types as a baseline: MedicalOrganization for the provider entity, Hospital for facilities, Physician (Person + jobTitle Physician with hasCredential) for clinicians, MedicalCondition for disease pages, MedicalProcedure for treatment pages, and Drug for medication pages. This gives LLMs the entity scaffolding they need to cite a healthcare brand confidently." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Citation Hierarchy LLMs Follow for Medical Topics", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#citation-hierarchy-medical", "description": "LLMs follow a clear citation hierarchy on medical topics: Tier 1 (PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews) > Tier 2 (NEJM, JAMA, The Lancet, BMJ) > Tier 3 (specialty society guidelines, peer-reviewed journals) > Tier 4 (provider content). Earning Tier 1 citations into your content — and being cited by Tier 2 — is what makes a healthcare brand visible inside AI medical answers." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "EU AI Act Annex III: Healthcare AI as High-Risk", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#eu-ai-act-healthcare", "description": "The EU AI Act (Regulation (EU) 2024/1689) classifies healthcare AI under Annex III as high-risk — including triage, diagnostic support, and medical devices. This imposes transparency, risk-management, conformity-assessment, and human-oversight obligations that LLMs increasingly cross-reference when citing health-tech brands." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Compliance as Citation Signal: HIPAA, GDPR, and Medical Disclaimers", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#compliance-disclaimers", "description": "HIPAA in the US, GDPR (with the special-category health data regime under Article 9) in the EU, and explicit medical disclaimers act as positive citation signals — not compliance noise. LLMs read these explicitly and treat them as evidence of trustworthiness, which is the gating E-E-A-T check on YMYL health content." } ], "speakable": { "@type": "SpeakableSpecification", "cssSelector": [ "[data-speakable='true']", "[data-snippet='true']", "[data-section-answer='true']", ".quick-answer", "h1" ] } }, { "@type": "BreadcrumbList", "@id": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#breadcrumb", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "ai-search", "item": "https://alicelabs.ai/en/insights/ai-search" }, { "@type": "ListItem", "position": 4, "name": "AI Search Optimization for Healthcare: The 2026 Medical LLMO Playbook", "item": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare" } ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#eric", "name": "Eric Lundberg", "jobTitle": "Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI automation", "url": "https://www.wikidata.org/wiki/Q1322483" }, { "@type": "DefinedTerm", "name": "Workflow automation", "url": "https://www.wikidata.org/wiki/Q120427660" }, { "@type": "DefinedTerm", "name": "Retrieval-Augmented Generation", "url": "https://www.wikidata.org/wiki/Q117761563" }, { "@type": "DefinedTerm", "name": "Enterprise AI implementation" } ], "sameAs": [ "https://www.linkedin.com/in/eric-lundberg-3530451bb/", "https://www.wikidata.org/wiki/Q140369978" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "jobTitle": "CEO & Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI agent orchestration", "url": "https://www.wikidata.org/wiki/Q98678395" }, { "@type": "DefinedTerm", "name": "AI strategy" }, { "@type": "DefinedTerm", "name": "AI search optimization (LLMO)" }, { "@type": "DefinedTerm", "name": "Enterprise AI strategy" }, { "@type": "DefinedTerm", "name": "AI consulting leadership" } ], "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Why is AI search optimization harder for healthcare than other industries?", "acceptedAnswer": { "@type": "Answer", "text": "Google classifies medical content as the highest tier of YMYL (Your Money or Your Life), holding it to the strongest possible E-E-A-T bar. ChatGPT, Claude, and Perplexity inherit and intensify that conservatism — they default to PubMed, WHO, NIH, CDC, and Cochrane Reviews rather than provider content on clinical topics. The compensating advantage is durability: once a credentialled provider enters the LLM citation set, it stays there longer than in faster-moving verticals." } }, { "@type": "Question", "name": "Which Schema.org medical types should healthcare sites implement?", "acceptedAnswer": { "@type": "Answer", "text": "Six types do most of the work. MedicalOrganization for the provider entity, Hospital for facilities, Physician (Person + jobTitle with hasCredential entries for MD and board certification) for clinicians, MedicalCondition for disease pages (with ICD-10/SNOMED codes), MedicalProcedure for treatment pages, and Drug for medication pages (with RxNorm/ATC codes). Layered, nested schema with structured codes beats flat Organization markup — it is the entity scaffolding LLMs use to cite healthcare content confidently." } }, { "@type": "Question", "name": "What clinician credentials matter most for healthcare LLMO?", "acceptedAnswer": { "@type": "Answer", "text": "MD, PhD, RN, board certifications (American Board of Medical Specialties, Royal College, EBA), licensure, and named hospital affiliations. Mark them up with Person + hasCredential schema linking to the certifying body, include them on clinician profile pages, and reference the clinician from every article they wrote or reviewed. This is the single most under-implemented YMYL signal we see in healthcare." } }, { "@type": "Question", "name": "Which sources do LLMs prefer to cite on medical topics?", "acceptedAnswer": { "@type": "Answer", "text": "A clear four-tier hierarchy. Tier 1: PubMed (NIH), WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews. Tier 2: top peer-reviewed journals (NEJM, JAMA, The Lancet, BMJ). Tier 3: specialty societies and peer-reviewed journals. Tier 4: provider, payer, and health-tech content. LLMs default to Tier 1 on medical prompts — earning citations into your content from Tier 1 and being mentioned by Tier 2 is what moves the needle." } }, { "@type": "Question", "name": "How does the EU AI Act affect healthcare AI tools and provider AI search?", "acceptedAnswer": { "@type": "Answer", "text": "Annex III of Regulation (EU) 2024/1689 classifies healthcare AI as high-risk — including triage, diagnostic support, and medical devices with AI components. This imposes transparency, risk-management, conformity-assessment, and human-oversight obligations. Providers and health-tech vendors operating in the EU must describe their AI tools consistently with Annex III. Vague 'AI-powered' marketing now carries regulatory exposure and reduces LLM citation likelihood, because LLMs cross-reference Annex III when answering safety prompts." } }, { "@type": "Question", "name": "Are HIPAA and GDPR disclosures positive or negative for LLM citation?", "acceptedAnswer": { "@type": "Answer", "text": "Positive — strongly. LLMs read HIPAA Notice of Privacy Practices, GDPR Article 9 health-data processing notices, and explicit medical disclaimers as evidence of trustworthiness, which is the gating E-E-A-T check on YMYL health content. Burying compliance content in microscopic footers is one of the most common and most wasteful LLMO mistakes in healthcare. Make these pages accessible, indexable, and authored by a named DPO or compliance officer." } }, { "@type": "Question", "name": "Does the Aggarwal et al. GEO research apply to healthcare?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — and arguably more strongly than in any non-YMYL vertical. Aggarwal et al. (2024) found citation-rich, statistic-rich, and quotation-rich content delivered up to 40% visibility lift across generative engines (arXiv:2311.09735). Healthcare benefits disproportionately because LLMs already default to PubMed-, WHO-, and Cochrane-anchored answers — well-cited medical content matches that preference natively rather than fighting against it." } }, { "@type": "Question", "name": "What is the single highest-leverage fix for a healthcare brand starting LLMO?", "acceptedAnswer": { "@type": "Answer", "text": "Clinician profile pages with structured credential markup. Most healthcare content teams under-invest in clinician pages, do not implement Person + hasCredential schema for MD and board certification, and link inconsistently from articles to clinician bios. Fixing this — naming credentialled clinicians, marking up credentials in structured data, and linking every clinical article to its named author or reviewer — is the highest-leverage single change for a YMYL healthcare brand starting LLMO work." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "AI Search Optimization for Healthcare", "description": "AI search optimization for healthcare in 2026: how YMYL, clinician E-E-A-T, MedicalOrganization and Physician schema, PubMed and WHO citations, EU AI Act high-risk classification, and HIPAA/GDPR disclaimers shape medical AI visibility.", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare", "datePublished": "2026-05-06", "dateModified": "2026-09-13", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "ai search optimization healthcare", "healthcare llmo", "medical ai search", "ymyl ai search", "healthcare seo ai", "medicalorganization schema", "eu ai act healthcare" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/ai-search-optimization-guide", "name": "AI Search Optimization: Complete Guide for 2026" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/ai-search-optimization-financial-services", "name": "AI Search Optimization for Financial Services" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/eu-ai-act-compliance-checklist-2026", "name": "EU AI Act Compliance Checklist 2026" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 6, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Healthcare Sits at the Top of the YMYL Bar", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#healthcare-and-ymyl-bar" }, { "@type": "ListItem", "position": 2, "name": "E-E-A-T for Healthcare: MD, PhD, RN, Board Certifications", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#e-e-a-t-for-healthcare" }, { "@type": "ListItem", "position": 3, "name": "Schema.org Medical Types for Healthcare LLMO", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#schema-medical-types" }, { "@type": "ListItem", "position": 4, "name": "The Citation Hierarchy LLMs Follow for Medical Topics", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#citation-hierarchy-medical" }, { "@type": "ListItem", "position": 5, "name": "EU AI Act Annex III: Healthcare AI as High-Risk", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#eu-ai-act-healthcare" }, { "@type": "ListItem", "position": 6, "name": "Compliance as Citation Signal: HIPAA, GDPR, and Medical Disclaimers", "url": "https://alicelabs.ai/en/insights/ai-search-optimization-healthcare#compliance-disclaimers" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Search & LLMO", "item": "https://alicelabs.ai/en/insights/ai-search" }, { "@type": "ListItem", "position": 4, "name": "AI Search Optimization for Healthcare" } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ Insights ](/en/insights)[ Contact ](/#email-form) 1. [Home](/en) [Insights](/en/insights) [AI Search & LLMO](/en/insights/ai-search) AI Search Optimization for Healthcare AI Search & LLMO Deep Dive Fresh · Last reviewed: 13 September 2026 · 9d ago # AI Search Optimization for Healthcare ## TL;DR Quick Answer Cited by AI > AI search optimization for healthcare means earning citations in ChatGPT, Claude, Perplexity, and Google AI Overviews under the highest YMYL (Your Money or Your Life) quality bar Google applies. The discipline relies on demonstrable clinician E-E-A-T (MD, PhD, RN, board certifications), MedicalOrganization, Hospital, Physician, MedicalCondition, MedicalProcedure, and Drug schema, citations from Tier-1 medical sources (PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews), and EU AI Act Annex III compliance for healthcare AI tools classified as high-risk — including triage, diagnostic support, and medical devices. Healthcare sits at the very top of the LLMO difficulty curve. ChatGPT, Claude, Perplexity, and Google AI Overviews are deliberately conservative on medical questions - they default to PubMed, WHO, NIH, CDC, and Cochrane over hospital marketing pages. This deepdive shows how providers, payers, and medical brands earn citations under YMYL, clinician E-E-A-T, and EU AI Act high-risk constraints. AI search optimization for healthcare is the practice of earning citations in AI answer engines (ChatGPT, Claude, Perplexity, Google AI Overviews, Gemini) for medical and clinical topics under Google's Your Money or Your Life (YMYL) quality bar — the highest tier in Google's E-E-A-T framework. It combines clinician E-E-A-T (MD, PhD, RN, board certifications), MedicalOrganization, Hospital, Physician, MedicalCondition, MedicalProcedure, and Drug schema, citation from Tier-1 medical sources (PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane), and EU AI Act Annex III compliance for healthcare AI classified as high-risk. ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Published May 6, 2026 · Updated September 13, 2026 13 min read Up to 40% Visibility lift from citation-rich content in generative engines (peer-reviewed) [Aggarwal et al. 2024 (arXiv:2311.09735)](https://arxiv.org/abs/2311.09735) Highest YMYL bar Health content held to Google's highest demonstrable-expertise standard under the E-E-A-T framework [Google Search Quality Rater Guidelines (E-E-A-T, YMYL)](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) Annex III high-risk EU AI Act classifies healthcare AI (triage, diagnostic support, medical devices) as high-risk [Regulation (EU) 2024/1689, Annex III (Artificial Intelligence Act)](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689) What you'll learn(6 points) - Why healthcare sits at the very top of Google's YMYL quality bar — and what that means for LLM citation behaviour - How E-E-A-T applies specifically to medicine: MD, PhD, RN, board certifications, and named clinical authorship - Which Schema.org medical types matter: MedicalOrganization, Hospital, Physician, MedicalCondition, MedicalProcedure, Drug - The citation hierarchy LLMs actually follow for health: PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane → NEJM, JAMA, Lancet, BMJ → brand - How HIPAA, GDPR, and clinical disclaimers act as positive citation signals — not compliance noise - How EU AI Act Annex III reshapes healthcare AI — triage, diagnostic support, and medical devices as high-risk ## Key Takeaways - 01 Health content sits at the top of YMYL — Google holds it to the highest demonstrable-expertise bar in the Search Quality Rater Guidelines, and LLMs mirror that conservatism aggressively on clinical prompts. - 02 Clinician credentials are non-negotiable. MD, PhD, RN, and named board certifications materially shift LLM citation likelihood; anonymous 'medical team' bylines are functionally invisible. - 03 Schema.org medical types — MedicalOrganization, Hospital, Physician, MedicalCondition, MedicalProcedure, and Drug — give LLMs the entity scaffolding they need to cite a healthcare brand confidently. - 04 Tier 1 medical citation sources (PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews) outweigh Tier 2 (NEJM, JAMA, The Lancet, BMJ) in LLM clinical answers — and both outweigh hospital marketing pages by a wide margin. - 05 Aggarwal et al. (2024) found citation-rich content lifted generative-engine visibility by up to 40% (arXiv:2311.09735) — health benefits disproportionately because LLMs already default to peer-reviewed answers. - 06 EU AI Act Annex III classifies healthcare AI — triage, diagnostic support, medical devices — as high-risk, requiring transparency, oversight, and conformity that LLMs increasingly cross-reference when citing health-tech brands. ### Contents 13 min left - [01 Healthcare Sits at the Top of the YMYL Bar ](#healthcare-and-ymyl-bar) - [02 E-E-A-T for Healthcare: MD, PhD, RN, Board Certifications ](#e-e-a-t-for-healthcare) - [03 Schema.org Medical Types for Healthcare LLMO ](#schema-medical-types) - [04 The Citation Hierarchy LLMs Follow for Medical Topics ](#citation-hierarchy-medical) - [05 EU AI Act Annex III: Healthcare AI as High-Risk ](#eu-ai-act-healthcare) - [06 Compliance as Citation Signal: HIPAA, GDPR, and Medical Disclaimers ](#compliance-disclaimers) Part of [AI Search Optimization: The Complete Guide for 2026](/en/insights/ai-search-optimization-guide) 01 / 06 Chapter ## Healthcare Sits at the Top of the YMYL Bar Google classifies medical and health content as the highest tier of YMYL — the Search Quality Rater Guidelines explicitly call out medical information as content that can directly affect a person's health and safety. ChatGPT, Claude, and Perplexity inherit and intensify that conservatism, defaulting to PubMed, WHO, NIH, and CDC over hospital marketing pages. Google's Search Quality Rater Guidelines define YMYL — Your Money or Your Life — as content that can affect financial stability, health, or safety. Medical content sits in the highest tier of that category. For YMYL health content, Google instructs raters to demand the strongest possible E-E-A-T evidence. The signal propagates to AI search because the underlying ranking models are trained on the same quality framework. Large language models then add their own clinical conservatism. ChatGPT, Claude, and Perplexity are explicitly tuned to be cautious on medical advice — they prefer PubMed-indexed studies, WHO and NIH guidance, and Cochrane systematic reviews over hospital marketing pages. This dynamic plays out three ways in healthcare LLMO. Each shapes how providers, payers, and medical brands should structure their content. - **Source preference is asymmetric.** A page citing PubMed, WHO, or NIH outranks the same claim citing a generic health blog by a wide margin. - **Clinician identity matters more than length.** An author with MD, board certification, and named affiliation is cited more readily than an anonymous "medical team" byline. - **Disclaimers are signal, not noise.** Clear "consult your physician" framing and HIPAA/GDPR disclosures make a brand safer to cite, not less authoritative. The honest framing for healthcare content teams is that LLM citation is harder to win in health than in any other vertical. The compensating advantage is durability — once a credentialled provider enters the LLM citation set, it stays there, which is why healthcare marketers increasingly bring in an [AI search optimization consultant](/en/ai-search) with YMYL experience rather than a generalist SEO agency. YMYL health content has the highest bar Google applies Treat YMYL health as a structural input, not a compliance checkbox. Pages without verifiable clinician credentials, peer-reviewed citations, or transparent medical disclaimers will not be cited by ChatGPT, Claude, or Perplexity — regardless of length, design, or keyword optimization. 02 / 06 Chapter ## E-E-A-T for Healthcare: MD, PhD, RN, Board Certifications In short E-E-A-T for healthcare demands four signals LLMs can verify: real clinicians with MD, PhD, RN, or board certifications, peer-reviewed citations on every load-bearing claim, named clinical experience, and transparent disclaimers about medical advice and the physician-patient relationship. E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is the closest public approximation of how LLMs reason about medical source quality. For health, all four letters are load-bearing. We work through them in the order LLMs appear to weight them for YMYL medical content. Experience and Expertise come first, then Authoritativeness, with Trustworthiness as the gating check. **Experience.** First-hand clinical experience. For a provider this means a named physician, nurse, or allied-health professional authoring or reviewing the content — not a generic "medical team" byline or unsigned marketing post. **Expertise.** Verifiable credentials. MD, PhD, RN, board certifications (American Board of Medical Specialties, Royal College, EBA), licensure, hospital affiliations, and specific clinical specialties. These should appear in author schema and on the clinician profile page. **Authoritativeness.** Citation patterns. A health page is authoritative when it cites primary sources — PubMed-indexed studies with PMIDs, WHO and NIH guidance with full reference, Cochrane systematic reviews, and clinical guidelines (NICE, USPSTF) — and when those sources cross-reference the brand's own published research. **Trustworthiness.** Disclosure and transparency. "Consult your physician" framing, HIPAA and GDPR notices, conflict-of-interest disclosures, regulatory status, and clear scope-of-practice statements. LLMs read these and treat them as positive signals. Most healthcare content marketing teams under-invest in clinician profile pages and credential markup. That is the single highest-leverage fix available to a provider starting healthcare LLMO work. Clinician profile pages are healthcare LLMO infrastructure Build clinician profile pages with MD/PhD/RN credentials, board certifications, hospital affiliations, specialties, publications, and structured Person + Physician schema. Then link every article and patient resource to the clinician who wrote or reviewed it. LLMs use this graph to assess whether the human behind a medical claim is real, named, and credentialled. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 03 / 06 Chapter ## Schema.org Medical Types for Healthcare LLMO In short Healthcare sites should ship six Schema.org medical types as a baseline: MedicalOrganization for the provider entity, Hospital for facilities, Physician (Person + jobTitle Physician with hasCredential) for clinicians, MedicalCondition for disease pages, MedicalProcedure for treatment pages, and Drug for medication pages. This gives LLMs the entity scaffolding they need to cite a healthcare brand confidently. Schema.org has a richer medical vocabulary than almost any other vertical. For healthcare LLMO, six types do most of the work — and they are documented at schema.org with JSON-LD examples. Implementation is JSON-LD in the page head, validated through the Schema Markup Validator. Layered, nested schema beats flat or generic Organization markup by a wide margin. **1\. MedicalOrganization.** The organisation type for hospitals, clinics, and medical groups. It signals to the LLM that the entity is a regulated healthcare provider, not a generic Organization or LocalBusiness. **2\. Hospital.** A subclass of MedicalOrganization for inpatient facilities. Use it with full address, geo, departments, medicalSpecialty, and availableService entries for each clinical service. **3\. Physician (Person + jobTitle).** Schema.org Person with jobTitle "Physician" plus hasCredential entries for MD, board certification, and licensure. List specialties with medicalSpecialty and affiliations with affiliation. **4\. MedicalCondition.** Disease and condition pages — diabetes, atrial fibrillation, depression. Use associatedAnatomy, signOrSymptom, possibleTreatment, riskFactor, and code (ICD-10, SNOMED CT) for granularity. **5\. MedicalProcedure.** Treatment pages — surgical procedures, diagnostic procedures, therapeutic interventions. Use procedureType, bodyLocation, and preparation entries. **6\. Drug.** Medication pages — with activeIngredient, mechanismOfAction, dosageForm, prescribingInfo, and code (RxNorm, ATC). Reference the regulatory status (FDA-approved, EMA-authorised) explicitly. One implementation note. hasCredential on Physician markup and code (ICD-10, SNOMED, RxNorm) on condition and drug markup are the most consistently under-implemented signals we see — and the highest-leverage to add. ICD-10, SNOMED, and RxNorm codes are healthcare's missing schema Most provider sites publish detailed condition and medication content without ever attaching ICD-10, SNOMED CT, or RxNorm codes via schema. Adding these — code on MedicalCondition, code on Drug, hasCredential on Physician — is the single highest-leverage schema fix available to a YMYL healthcare brand. 04 / 06 Chapter ## The Citation Hierarchy LLMs Follow for Medical Topics In short LLMs follow a clear citation hierarchy on medical topics: Tier 1 (PubMed, WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews) > Tier 2 (NEJM, JAMA, The Lancet, BMJ) > Tier 3 (specialty society guidelines, peer-reviewed journals) > Tier 4 (provider content). Earning Tier 1 citations into your content — and being cited by Tier 2 — is what makes a healthcare brand visible inside AI medical answers. LLMs do not weight all sources equally on medical topics. The citation pattern across ChatGPT, Claude, Perplexity, and Google AI Overviews is more rigid in healthcare than in any other vertical we measure. The four-tier hierarchy below reflects what we observe in the Alice Labs LLMO Citation Benchmark for healthcare — the same benchmark that drives [our enterprise ai seo](/en/ai-seo) engagements. It is the practical guide to which sources to cite in your content — and which sources you need to be cited by. Editorial teams operationalising the hierarchy across large libraries can pair it with purpose-built [ai content optimization tools](/en/insights/ai-content-optimization-tools) for entity coverage and citation scoring. **Tier 1: Public health authorities and evidence synthesis.** PubMed (NIH), WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews. These are the LLM default anchors for clinical questions. **Tier 2: Top peer-reviewed medical journals.** NEJM, JAMA, The Lancet, BMJ. Heavily cited as secondary sources for primary research, particularly recent trials and major clinical studies. **Tier 3: Specialty societies and peer-reviewed journals.** American Heart Association, ESC, AAP, ACOG, specialty-specific journals. Cited for sub-specialty guidance when broader guidelines are silent. **Tier 4: Provider content.** Hospital websites, health-system marketing, payer pages, health-tech vendor pages. Cited when they carry strong clinician credentials and reference Tier 1 and Tier 2 sources directly. One strategic implication. Most healthcare content teams target Tier 4 (their own page) ranking. The leverage is in citing Tier 1 inside your content and earning Tier 2 mentions — both move you up the hierarchy LLMs use. Tier 1 wins because LLMs are conservative on medical advice Across ChatGPT, Claude, Perplexity, and Google AI Overviews, medical prompts default to PubMed, WHO, NIH, CDC, and Cochrane before any provider content. This is a deliberate safety-tuning choice in the underlying models — not a ranking quirk you can outflank with content volume. Medical citation source authority — how LLMs weight sources on YMYL health topics Tier Source category Examples Typical LLM treatment Tier 1 Public health authorities and evidence synthesis PubMed (NIH), WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews Default trusted source — cited verbatim, anchored as primary clinical reference Tier 2 Top peer-reviewed medical journals NEJM, JAMA, The Lancet, BMJ Cited heavily for primary research and recent trials; functions as secondary anchor Tier 3 Specialty societies and peer-reviewed journals AHA, ESC, AAP, ACOG, specialty-specific peer-reviewed journals Cited for sub-specialty guidance when broader guidelines are silent Tier 4 Provider, payer, and health-tech content Hospital sites, health-system marketing, payer pages, health-tech vendor pages Cited when clinician credentials are strong and Tier 1/2 sources are referenced directly Source: Alice Labs LLMO Citation Benchmark — healthcare observations across ChatGPT, Claude, Perplexity, Google AI Overviews ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Want a healthcare LLMO Citation Benchmark for your provider or health-tech brand? We run a quarterly citation benchmark tuned for YMYL healthcare - PubMed and Cochrane citation density, clinician credential markup, MedicalOrganization and Physician schema, ICD-10/SNOMED/RxNorm coding coverage, and EU AI Act Annex III disclosure clarity. The output is a sensitivity-ranked roadmap your medical and compliance teams can sign off on. [Request a Healthcare LLMO Benchmark](#contact) 05 / 06 Chapter ## EU AI Act Annex III: Healthcare AI as High-Risk In short The EU AI Act (Regulation (EU) 2024/1689) classifies healthcare AI under Annex III as high-risk — including triage, diagnostic support, and medical devices. This imposes transparency, risk-management, conformity-assessment, and human-oversight obligations that LLMs increasingly cross-reference when citing health-tech brands. The EU AI Act — Regulation (EU) 2024/1689 — entered into force in 2024 with a phased application timeline. Annex III lists the high-risk AI use cases, and healthcare features prominently. Three categories of healthcare AI tools attract specific high-risk scrutiny. All three impose transparency, risk-management, and human-oversight obligations that should be visible on the public product page. **AI-assisted triage.** Tools that prioritise patients in emergency departments, primary care, or telehealth. The transparency obligation is to disclose data sources, performance characteristics, and known limitations across patient populations. **Diagnostic support.** AI tools that interpret imaging, laboratory results, or pathology slides. The human-oversight obligation requires that the clinician remains the decision-maker, with AI output positioned as decision support — not autonomous diagnosis. **Medical devices with AI components.** Software as a Medical Device (SaMD) and AI-enabled hardware. These sit at the intersection of the EU AI Act and the Medical Device Regulation (MDR/IVDR), with stacked conformity assessment requirements. For LLMO, the implication is that brand pages describing healthcare AI tools must be precise. LLMs increasingly cross-reference Annex III when answering "is this medical AI tool safe to use" prompts — vague AI marketing language becomes a liability rather than an asset. One pragmatic recommendation. Publish a public AI use-case register for healthcare AI tools, with Annex III classification, oversight arrangements, conformity assessment status, and known limitations. Link it from product pages. Vague healthcare-AI marketing now carries Annex III exposure Phrases like 'AI-powered diagnosis' on a vendor homepage carry direct EU AI Act exposure — and LLMs read both the marketing page and any regulator-aware disclosures. Inconsistency reduces citation likelihood and increases regulatory risk simultaneously. The fix is precise, classification-aware language with a public AI use-case register. ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 06 / 06 Chapter ## Compliance as Citation Signal: HIPAA, GDPR, and Medical Disclaimers In short HIPAA in the US, GDPR (with the special-category health data regime under Article 9) in the EU, and explicit medical disclaimers act as positive citation signals — not compliance noise. LLMs read these explicitly and treat them as evidence of trustworthiness, which is the gating E-E-A-T check on YMYL health content. Healthcare compliance content is often hidden in footers and policy pages, treated as something to bury. For LLMO, this is the wrong instinct — clear, accessible compliance disclosures are read by LLMs as positive trust signals. Three categories of compliance content carry the most citation weight. Each maps to a specific regulatory regime that LLMs are tuned to recognise. **HIPAA disclosures (US).** Notice of Privacy Practices, Business Associate Agreement information, patient rights summary. Make them accessible from every patient-facing page, not just buried in the footer. **GDPR Article 9 disclosures (EU).** Health data is a special category under GDPR Article 9 — separate consent, separate lawful basis. Publish a clear health-data processing notice with explicit Article 9 lawful basis, data residency, and transfer mechanisms. **Medical disclaimers and scope-of-practice statements.** "Consult your physician," "this is not medical advice," and explicit scope-of-practice statements ("this clinic treats adults; paediatric questions should be directed to a paediatrician") are positive citation signals. One operational consequence. Compliance pages should be indexable, well-structured, and authored by a named compliance officer or DPO. Hidden or boilerplate compliance content is a missed LLMO signal, not a smart legal hedge. Surface compliance content - do not bury it Make HIPAA, GDPR Article 9, and medical disclaimers accessible from every patient-facing page, with structured author markup naming the DPO, compliance officer, or clinician responsible. LLMs read these as positive trust signals, and burying them in microscopic footers is one of the most common — and most wasteful — LLMO mistakes in healthcare. ## About the Authors & Reviewers Published May 6, 2026 · Updated September 13, 2026 Written by ![Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by September 13, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published May 6, 2026 · Updated September 13, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### Why is AI search optimization harder for healthcare than other industries? ▾ Google classifies medical content as the highest tier of YMYL (Your Money or Your Life), holding it to the strongest possible E-E-A-T bar. ChatGPT, Claude, and Perplexity inherit and intensify that conservatism — they default to PubMed, WHO, NIH, CDC, and Cochrane Reviews rather than provider content on clinical topics. The compensating advantage is durability: once a credentialled provider enters the LLM citation set, it stays there longer than in faster-moving verticals. ### Which Schema.org medical types should healthcare sites implement? ▾ Six types do most of the work. MedicalOrganization for the provider entity, Hospital for facilities, Physician (Person + jobTitle with hasCredential entries for MD and board certification) for clinicians, MedicalCondition for disease pages (with ICD-10/SNOMED codes), MedicalProcedure for treatment pages, and Drug for medication pages (with RxNorm/ATC codes). Layered, nested schema with structured codes beats flat Organization markup — it is the entity scaffolding LLMs use to cite healthcare content confidently. ### What clinician credentials matter most for healthcare LLMO? ▾ MD, PhD, RN, board certifications (American Board of Medical Specialties, Royal College, EBA), licensure, and named hospital affiliations. Mark them up with Person + hasCredential schema linking to the certifying body, include them on clinician profile pages, and reference the clinician from every article they wrote or reviewed. This is the single most under-implemented YMYL signal we see in healthcare. ### Which sources do LLMs prefer to cite on medical topics? ▾ A clear four-tier hierarchy. Tier 1: PubMed (NIH), WHO, NIH, CDC, ECDC, NICE, Cochrane Reviews. Tier 2: top peer-reviewed journals (NEJM, JAMA, The Lancet, BMJ). Tier 3: specialty societies and peer-reviewed journals. Tier 4: provider, payer, and health-tech content. LLMs default to Tier 1 on medical prompts — earning citations into your content from Tier 1 and being mentioned by Tier 2 is what moves the needle. ### How does the EU AI Act affect healthcare AI tools and provider AI search? ▾ Annex III of Regulation (EU) 2024/1689 classifies healthcare AI as high-risk — including triage, diagnostic support, and medical devices with AI components. This imposes transparency, risk-management, conformity-assessment, and human-oversight obligations. Providers and health-tech vendors operating in the EU must describe their AI tools consistently with Annex III. Vague 'AI-powered' marketing now carries regulatory exposure and reduces LLM citation likelihood, because LLMs cross-reference Annex III when answering safety prompts. ### Are HIPAA and GDPR disclosures positive or negative for LLM citation? ▾ Positive — strongly. LLMs read HIPAA Notice of Privacy Practices, GDPR Article 9 health-data processing notices, and explicit medical disclaimers as evidence of trustworthiness, which is the gating E-E-A-T check on YMYL health content. Burying compliance content in microscopic footers is one of the most common and most wasteful LLMO mistakes in healthcare. Make these pages accessible, indexable, and authored by a named DPO or compliance officer. ### Does the Aggarwal et al. GEO research apply to healthcare? ▾ Yes — and arguably more strongly than in any non-YMYL vertical. Aggarwal et al. (2024) found citation-rich, statistic-rich, and quotation-rich content delivered up to 40% visibility lift across generative engines (arXiv:2311.09735). Healthcare benefits disproportionately because LLMs already default to PubMed-, WHO-, and Cochrane-anchored answers — well-cited medical content matches that preference natively rather than fighting against it. ### What is the single highest-leverage fix for a healthcare brand starting LLMO? ▾ Clinician profile pages with structured credential markup. Most healthcare content teams under-invest in clinician pages, do not implement Person + hasCredential schema for MD and board certification, and link inconsistently from articles to clinician bios. Fixing this — naming credentialled clinicians, marking up credentials in structured data, and linking every clinical article to its named author or reviewer — is the highest-leverage single change for a YMYL healthcare brand starting LLMO work. [Previous in AI Search & LLMO ### Bing Copilot Optimization: Get Cited in Microsoft AI (2026) ](/en/insights/bing-copilot-optimization)[Next in AI Search & LLMO ### Site Architecture for AI Crawlers: Hub-and-Spoke for LLMs ](/en/insights/site-architecture-for-ai-crawlers) ## Further reading - [GEO: Generative Engine Optimization (Aggarwal et al., 2024)](https://arxiv.org/abs/2311.09735)· arxiv.org - [Schema.org — MedicalOrganization class](https://schema.org/MedicalOrganization)· schema.org - [EUR-Lex — Regulation (EU) 2024/1689 (AI Act, Annex III)](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)· eur-lex.europa.eu ## Related reading [pillar ### AI Search Optimization: Complete Guide for 2026 Full playbook covering ChatGPT, Perplexity, Claude, and Google AI Overviews. 14 min](/en/insights/ai-search-optimization-guide) [deepdive ### AI Search Optimization for Financial Services Sister YMYL deepdive — fintech and banking LLMO under E-E-A-T and EU AI Act constraints. 13 min](/en/insights/ai-search-optimization-financial-services) [deepdive ### EU AI Act Compliance Checklist 2026 Compliance checklist for Regulation (EU) 2024/1689 — directly relevant to Annex III healthcare AI. 12 min ](/en/insights/eu-ai-act-compliance-checklist-2026) ## Sources 1. [Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024)](https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 2. [Schema.org — MedicalOrganization, Hospital, Physician, MedicalCondition, MedicalProcedure, Drug](https://schema.org/MedicalOrganization)(accessed 2026-05-06) 3. [Regulation (EU) 2024/1689 — Artificial Intelligence Act (Annex III, high-risk AI systems)](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)(accessed 2026-05-06) 4. [Google — Search Quality Rater Guidelines (E-E-A-T, YMYL)](https://developers.google.com/search/docs/fundamentals/creating-helpful-content)(accessed 2026-05-06) 5. [PubMed (US National Library of Medicine, NIH)](https://pubmed.ncbi.nlm.nih.gov)(accessed 2026-05-06) 6. [World Health Organization (WHO) — clinical guidance and global health data](https://www.who.int)(accessed 2026-05-06) 7. [US National Institutes of Health (NIH)](https://www.nih.gov)(accessed 2026-05-06) 8. [US Centers for Disease Control and Prevention (CDC)](https://www.cdc.gov)(accessed 2026-05-06) 9. [European Centre for Disease Prevention and Control (ECDC)](https://www.ecdc.europa.eu)(accessed 2026-05-06) 10. [National Institute for Health and Care Excellence (NICE, UK)](https://www.nice.org.uk)(accessed 2026-05-06) 11. [Cochrane Reviews — systematic reviews of healthcare interventions](https://www.cochranelibrary.com)(accessed 2026-05-06) 12. [New England Journal of Medicine (NEJM)](https://www.nejm.org)(accessed 2026-05-06) 13. [JAMA — Journal of the American Medical Association](https://jamanetwork.com)(accessed 2026-05-06) 14. [The Lancet](https://www.thelancet.com)(accessed 2026-05-06) 15. [The BMJ](https://www.bmj.com)(accessed 2026-05-06) 16. [llms.txt — Answer.AI proposal (Jeremy Howard, September 2024)](https://llmstxt.org)(accessed 2026-05-06) 17. [SparkToro / Datos — 2024 zero-click search analysis (Rand Fishkin)](https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)(accessed 2026-05-06) 18. [Alice Labs LLMO Citation Benchmark — quarterly tracking across ChatGPT, Claude, Perplexity, Google AI Overviews (healthcare variant)](https://alicelabs.ai/en/ai-search)(accessed 2026-05-06) Next scheduled review: 2026-12-12 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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