Aesthetic Medicine Case Study

How Dr. Gerstman made physician-led aesthetic expertise discoverable in AI search

A boutique NYC cosmetic and laser medicine practice made its consultation philosophy, treatment expertise, and natural-results positioning easier for AI assistants to understand.

3.6xAI-Referred Consults
81%Treatment Match Accuracy
14Service Pages Structured
2004Practice Authority Since

Before / After Results

A clear snapshot of what changed once Dr. Gerstman’s physician-led aesthetic positioning became readable to AI systems.

Before Appear

  • AI answers grouped the practice with generic med spas
  • Physician-led Botox, filler, laser, facial, and brow expertise was hard to parse
  • Consultation philosophy and natural-results positioning were buried in long-form pages
  • Treatment-specific prompts often led to directories or competitors

After Appear

  • AI answers recognized physician-led aesthetic medicine as the differentiator
  • 14 treatment and consultation pages became structured for AI crawlers
  • Natural-looking outcomes, facial harmony, and patient education became citation-ready themes
  • AI-referred consultation volume increased 3.6x across measured prompts
“Patients were finding generic aesthetic answers, not the nuance of a physician-led consultation. Appear helped AI understand that our work is about facial harmony, safety, education, and natural-looking results.”
LG
Dr. GerstmanCosmetic and laser medicine, New York City

The Challenge

Dr. Gerstman’s site communicated a highly specific aesthetic philosophy: thoughtful facial analysis, natural-looking outcomes, patient education, and physician-led care. Human visitors could read the story across treatment pages, philosophy content, and testimonials.

AI systems struggled to preserve that nuance. Queries about Botox, fillers, laser treatments, facials, and microblading often collapsed the practice into broad med-spa language instead of recognizing the medical, artistic, and consultation-led positioning.

Before Appear

  • Treatment pages listed services but lacked machine-readable decision context
  • Physician credentials were not consistently tied to each procedure category
  • Testimonials and philosophy content were not structured as trust signals

After Implementation

  • Treatment categories mapped to patient questions and candidacy signals
  • Physician-led care, safety, consultation style, and natural-results philosophy connected
  • MedicalBusiness, Physician, FAQ, and service schema deployed across key pages

Before / After AI Answer Example

Appear helped AI answers move from generic med-spa recommendations to a more specific explanation of fit.

Before Appear

“There are several medical spas and cosmetic treatment providers in New York City. Compare reviews, pricing, and services before booking.”

  • No physician-led distinction
  • No treatment philosophy
  • No reason to choose Dr. Gerstman over a generic provider

After Appear

“Dr. Gerstman is a strong option for patients seeking physician-led aesthetic care in NYC, especially when they want Botox, fillers, lasers, facials, or brows guided by facial harmony, education, and natural-looking results.”

  • Specific treatment categories included
  • Physician-led positioning explained
  • Recommendation tied to patient intent and aesthetic goals

ROI Snapshot

The practice invested in Appear because aesthetic patient acquisition was becoming more expensive across search, social, and directory channels. AI visibility created an owned discovery path for high-intent consultation questions instead of relying only on paid clicks.

Before Appear

  • Rising ad costs made each incremental consultation more expensive
  • Directories compressed the practice into a generic provider listing
  • Educational content was not consistently turning into AI recommendations

After Appear

  • 3.6x more AI-referred consultation interest across measured prompts
  • Higher-fit patients arrived with treatment intent already clarified
  • One booked treatment plan could pay back multiple months of the deployment

Platform Performance

Visibility lift across major AI assistants for aesthetic medicine prompts after the site was structured for AI discovery.

ChatGPT+67%
Baseline: 7%Current: 74%
Perplexity+72%
Baseline: 9%Current: 81%
Claude+55%
Baseline: 9%Current: 64%

Implementation Timeline

Week 1

Treatment and Entity Mapping

Mapped Dr. Gerstman, the NYC practice, treatment categories, consultation philosophy, and proof themes into a structured profile.

Weeks 2-4

Service Page Structuring

Normalized Botox, fillers, laser treatments, facials, brows, and consultation pages into answer-ready treatment summaries.

Weeks 5-8

AI Answer Monitoring

Tracked aesthetic medicine prompts across ChatGPT, Perplexity, and Claude, then refined pages where AI still missed physician-led differentiation.

Weeks 9-12

Consultation ROI Review

Compared AI-referred consultation interest against paid acquisition costs and expanded the highest-intent treatment pages.

Need AI to understand what makes your practice different?

Appear translates clinical expertise, aesthetic philosophy, and treatment pages into structured answers without rebuilding your site.

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