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Health Platform Becomes #1 AI-Cited Source for Dermatology in 5 Months

Consumer Health Platform · 2.4M monthly visitors · GP-reviewed content  ·  5 months

#1
cited source in ChatGPT and Perplexity for dermatology queries
89%
Citation Rate (was 12%)
3.4×
AI-Influenced Traffic
41 pts
E-E-A-T Score Lift
⚠️ The Challenge

The platform had 2.4M monthly visitors and GP-reviewed content but was bypassed by AI engines in favour of WebMD, Healthline, and Mayo Clinic. For dermatology queries — the highest-traffic category — citation rate was 12%. Content quality was high but the trust signals AI engines use to evaluate medical authority were almost entirely absent.

💡 The Solution

E-E-A-T Scorer diagnosed the root cause: no doctor credentials in structured data, no author schema, no clinical review dates in markup, no citations to peer-reviewed sources. AI medical citations require specific trust signals beyond content quality. A systematic E-E-A-T overhaul was designed across the dermatology category — 180 pages.

📈 The Result

E-E-A-T score lifted 41 points across the category. Citation rate: 12%→89% across 15 tracked dermatology queries in 5 months. Now the #1 cited source for dermatology in ChatGPT and Perplexity, ahead of WebMD for 9 of 15 queries.

How They Did It

Step-by-step breakdown of the exact approach taken.

🏥
E-E-A-T Medical Authority Audit — 180 Pages

E-E-A-T Scorer audited every dermatology page against healthcare-specific authority signals. Findings: 0% of pages had Person schema for the reviewing doctor, 0% had clinical review dates in structured data, 0% cited peer-reviewed sources. These are the signals AI engines weight heavily for medical content.

👩‍⚕️
Doctor Credentials Added as Structured Data

Person + MedicalOrganization schema added to all 180 pages, linking each article to the reviewing doctor's credentials and publication history. Schema Markup Builder generated the JSON-LD. This single change produced the largest E-E-A-T improvement of any intervention in the programme.

📅
Clinical Review Dates and Update Signals

Content Freshness Auditor: 60% of pages had no "last reviewed" date and no dateModified in schema. Medical content without review signals is treated as potentially outdated by AI engines. Review dates and update schema added to all pages. Freshness score improved significantly across the category.

📚
Peer-Reviewed Citations on Top 40 Pages

Information Gain Scorer showed all competitor pages cited by AI engines included references to peer-reviewed studies. The top 40 dermatology pages were updated with 2–4 relevant PubMed citations each. Citation anchoring to authoritative sources is a strong AI trust signal in healthcare content.

🤖
AI Prompt Monitoring — 15 Clinical Queries Tracked Weekly

AI Citation Hub was configured to track citation rate across 15 core dermatology prompts weekly. This gave the content team measurable feedback on which page changes were moving the needle. Monitoring dashboard became the primary editorial KPI for the entire category.

The Full Results

Month 1: Schema and credentials deployed. Citation rate 12%→29%. Month 2: Peer-reviewed citations on top 40 pages. Citation rate 29%→52%. Month 3: Full 180-page rollout. Citation rate 52%→71%. Month 4: Freshness signals indexed. Citation rate 71%→83%. Month 5: Compound authority effects. Citation rate 83%→89%. Now #1 ahead of WebMD for 9/15 queries. AI-influenced traffic: +3.4× year-on-year.

“We had the best content. We just did not have the signals. SEOGEO360 made us visible to AI engines by surfacing exactly which trust signals were missing — and gave us a page-by-page plan to add them.”

— Head of SEO, Consumer Health Platform

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