Complete reference guide to all trust signals, their sources, and implementation standards
Where:
| Category | Rules | Deduction per rule | Total possible deduction |
|---|---|---|---|
| CRITICAL | 5 (medicalwebpage, medicalentity, author, review, citations) | 20 pts | 100 pts |
| HIGH | 3 (jsonld, specialty, audience) | 15 pts | 45 pts |
| MEDIUM | 5 (publisher, disclaimer, dates, structure_h1, structure_h2) | 10 pts | 50 pts |
| LOW | 6 (author_credentials, citations_weak, dates_modified, structure_paragraphs, semantic_html, health_relevance) | 5 pts | 30 pts |
| INFO | 5 (structure_lists, internal_links, llms_txt, markdown, provenance) | 0 pts | 0 pts |
| TOTAL | 225 pts | ||
These signals are designed to help human readers assess trustworthiness through authorship, citations, transparency, and readability.
What we check: Presence of author name with medical credentials (MD, PhD, specialist certification) and affiliation.
Why it matters: Readers need to know who wrote the content. 78% of users check authorship before trusting medical information online. Anonymous content is 3x more likely to be considered unreliable.
What we check: Author name includes medical credentials (MD, PhD, Board Certified, etc.).
Why it matters: Credentials provide evidence of expertise. Google's E-E-A-T guidelines specifically require "Expertise" signals for YMYL (Your Money or Your Life) content.
What we check: Presence of a named medical reviewer with credentials (reviewedBy in JSON-LD or visible text).
Why it matters: Medical review adds accountability. 65% of users trust content more when they see it was reviewed by a medical professional.
What we check: Links to PubMed, WHO, government (.gov) sources, or DOIs (Digital Object Identifiers).
Why it matters: Citations provide verifiable evidence. 82% of readers check sources before trusting medical claims. Missing citations is the #1 red flag for medical misinformation.
What we check: Citations exist but are not from trusted sources (PubMed, WHO, .gov, DOI).
Why it matters: Not all sources are equal. Blog posts and news articles are not considered authoritative medical sources.
What we check: Publisher is clearly identified as a medical organization (hospital, university, research institute).
Why it matters: Knowing the publisher helps readers assess institutional authority. 72% of users trust medical info more when it comes from a known hospital or research institution.
What we check: Clear disclaimer stating content is informational and not a substitute for professional medical advice.
Why it matters: Legal protection and user safety. 58% of users may mistakenly use online medical info as professional advice. A clear disclaimer is both responsible and required by law in many jurisdictions.
What we check: Published date (datePublished in JSON-LD or visible text).
Why it matters: Medical knowledge evolves rapidly. 89% of users check the date before reading medical content. Articles older than 3 years may be outdated.
What we check: Last modified/updated date (dateModified in JSON-LD or visible text).
Why it matters: Modified dates show content is actively maintained. Outdated medical info can be dangerous. 74% of users prefer sites that show when content was last reviewed.
What we check: Presence of <h1> heading on the page.
Why it matters: H1 headings help users and search engines understand the page's main topic. 65% of users scan headings before reading. A missing H1 reduces accessibility and SEO.
<h1>Understanding Type 2 Diabetes</h1>
What we check: Presence of <h2> subheadings organizing content.
Why it matters: H2 headings organize content into scannable sections. 79% of users scan rather than read entire articles. Clear H2 headings make your content more accessible.
<h2>Common Symptoms</h2><h2>Treatment Options</h2>
What we check: Paragraphs over 150 words in length.
Why it matters: Long paragraphs are hard to read on screens. 55% of users will skip over paragraph blocks of text. Shorter paragraphs improve readability and engagement.
What we check: Presence of <ul> or <ol> lists.
Why it matters: Lists make content scannable. 70% of users prefer content with lists and bullet points. They also help with AI/LLM understanding of key points.
<ul><li>Increased thirst</li><li>Frequent urination</li></ul>
What we check: Presence of medical keywords in content (diabetes, cancer, treatment, symptoms, etc.).
Why it matters: Medical keywords help users find your content and help AI understand the topic. They also signal that your content is health-focused.
These signals help AI systems and LLMs properly understand, categorize, and trust your medical content.
What we check: @type: MedicalWebPage in JSON-LD structured data.
Why it matters: AI systems need explicit signals. MedicalWebPage tells AI this is medically relevant content, not just general articles. Without this, AI may treat it as generic content.
{"@type":"MedicalWebPage","medicineSystem":"Evidence-based medicine","medicalAudience":"Patient"}
What we check: Medical entity type defined (MedicalCondition, MedicalTherapy, MedicalGuideline, ScholarlyArticle).
Why it matters: AI systems need to know the specific medical subject. Defining the entity helps AI correctly categorize and understand the content.
{"@type":"MedicalCondition","name":"Type 2 Diabetes Mellitus"}
What we check: Presence of <script type="application/ld+json"> tags.
Why it matters: JSON-LD is the standard for machine-readable data. AI systems like Google's search use it to understand page context. Without it, AI has to guess.
<script type="application/ld+json">{"@context":"https://schema.org","@type":"MedicalWebPage"}</script>
What we check: relevantSpecialty property in JSON-LD.
Why it matters: MedicalSpecialty helps AI understand the domain. A cardiology page and an endocrinology page should be treated differently. It improves AI accuracy.
{"relevantSpecialty":{"@type":"MedicalSpecialty","name":"Cardiology"}}
What we check: medicalAudience property in JSON-LD.
Why it matters: MedicalAudience tells AI who the content is for. Patient content should be simpler, physician content more technical. This improves AI recommendations.
{"medicalAudience":"Patient"} β for public health content
What we check: Use of semantic HTML elements (<article>, <section>, <nav>, <header>, <footer>, <main>, <aside>).
Why it matters: Semantic HTML helps AI understand page structure. <article> clearly indicates main content. <section> organizes sections. This improves accessibility and AI understanding.
<article><header>Title</header><section>Content</section></article>
What we check: Number of internal links (links to other pages on the same site).
Why it matters: Internal links create a knowledge graph. AI can understand relationships between topics. Users can navigate to related content. It also helps with SEO.
<a href="/diabetes-type-1">Learn about Type 1 Diabetes</a>
What we check: Presence of link to /llms.txt file.
Why it matters: /llms.txt is a standard AI entry point. It tells AI which pages are most important. Think of it as a sitemap specifically for AI systems.
<a href="/llms.txt">llms.txt for AI</a>
What we check: Link to .md markdown version of the content.
Why it matters: AI systems parse markdown more efficiently than HTML. Clean markdown reduces parsing errors and helps AI extract key information.
<a href="/content.md">Download Markdown version</a>
What we check: PROV-O structured data (prov:Activity type).
Why it matters: PROV-O provides detailed provenance (who did what, when). AI systems can track content history. This is particularly important for medical content that gets updated regularly.
{"@context":"http://www.w3.org/ns/prov","@type":"Activity","wasAssociatedWith":{"name":"Dr. Smith"}}
Our methodology is based on established trust frameworks:
Eight principles for trustworthy health websites:
Four pillars for assessing content quality, especially for YMYL (Your Money or Your Life) topics like health:
Standardized structured data for medical content:
Standard for tracking provenance and content history:
Please read carefully: This tool has fundamental limitations that every user must understand.
No automated tool can verify if medical information is factually correct. Our tool checks trust signals (authorship, citations, dates, disclaimers), not whether the content is medically accurate.
Example: A well-structured page with an MD author, citations, and JSON-LD could still contain incorrect medical advice. Always consult a real doctor.
The following websites block automated requests:
β For these sites, we provide trust estimates based on domain reputation and step-by-step manual verification instructions.
If a page claims "Dr. John Smith, MD" β we detect the title, but we cannot verify that John Smith actually holds an MD degree. Anyone can add "MD" to their name online.
Modern React/Vue/Angular sites that render content dynamically may not be fully analyzable. We only see the initial HTML response.
If a page requires login or payment, we can only analyze metadata in the <head> section, not the full content.
Older content is not automatically untrustworthy. Classical medical literature and landmark clinical trials remain highly valuable. The tool applies a historical document bonus for significant older papers.