How Dermatologists Get Found on ChatGPT and AI Search

The Skin Care Discovery Shift

Three years ago, a patient concerned about a changing mole would type "dermatologist near me" into Google and scan the map pack. Today, that same patient opens ChatGPT on their phone and types: "Who is the best board-certified dermatologist for suspicious moles and skin cancer screening in Austin, Texas?"

The shift is quiet. It does not appear in your call volume data until the trend has already compounded. Public interest in AI dermatology grew 143.6 percent in 2023 and another 59.12 percent in 2024. Patients are not just curious about AI skin tools. They are using AI as their first point of contact for choosing a provider.

ChatGPT now handles between 1.6 and 1.8 billion monthly visits. A meaningful slice of those queries are health-related, and a growing portion name specific specialties and cities. Dermatology practices that are not findable through AI are invisible to an enormous and rapidly growing discovery channel.

The patterns driving this shift are not unique to dermatology. Understanding how medical practices get found on AI search provides useful context for the signals that determine visibility across healthcare specialties broadly.

How Patients Find Dermatologists via AI Today

The discovery journey for a dermatology patient in 2026 has a new first step that most practices have not accounted for. It starts not with a search engine but with a conversational AI query. The funnel looks like this:

Research confirms the trust dynamic: 73.8 percent of patients say they trust dermatologist-guided AI recommendations over standalone AI tools. This means patients are not blindly following AI. They are using it as a shortlisting mechanism and then trusting that shortlist. If your practice is on it, you get the call. If not, you never existed in that patient's journey.

The Entity Authority Problem for Dermatology Practices

AI platforms do not search for "a good dermatologist." They retrieve information about specific entities: named practices with defined specialties, verifiable credentials, and consistent descriptions across public sources. If your practice is not clearly defined as a distinct entity in the data AI can access, you are functionally invisible.

Entity fragmentation is one of the most common and least understood problems in dermatology AI visibility. It happens when:

• ▸Your practice name appears differently across directories (for example, "Smith Dermatology" vs. "Dr. Sarah Smith, MD" vs. "Smith Skin Clinic")
• ▸Your specialty is described inconsistently: "skin doctor," "dermatology office," and "board-certified dermatologist" are treated as different entities by AI
• ▸Your address, phone number, or provider names differ between your website, Google Business Profile, and third-party directories
• ▸You have multiple providers but no clear entity structure connecting them to a single practice location with a consistent profile

The entity authority problem applies broadly across healthcare specialties. The parallels are particularly instructive in how cosmetic surgeons approach AI visibility, where credential fragmentation across platforms produces nearly identical invisibility patterns.

Why Specialty Signals Are the Differentiator

"Dermatologist" is not specific enough. AI search rewards practices that signal granular expertise in defined conditions. When a patient asks about eczema, ChatGPT is not looking for the nearest dermatology office. It is looking for a practice whose public signal footprint is clearly associated with eczema treatment.

The practices that dominate AI recommendations in dermatology are almost never the ones marketing themselves as general "full-service dermatology." They are the ones who have built clear, distributed signals around specific conditions and procedures. The categories that generate the highest AI citation rates include:

This does not mean a general dermatology practice cannot appear in AI results. It means the practice needs to build specialty-specific signal clusters for each condition it treats. A practice that signals expertise in both Mohs surgery and pediatric dermatology can capture both query types. A practice that signals expertise in nothing specific captures neither.

The Pharma Dominance Problem

Here is the structural challenge no dermatology practice can ignore: two pharmaceutical companies, AbbVie and Galderma, control approximately 80 percent of aesthetic medicine AI citations in 2026. They publish high-volume, authoritative content at scale across hundreds of platforms simultaneously, and their content is built specifically to be indexed by AI retrieval systems.

That leaves individual dermatology practices competing for the remaining 20 percent of AI citation space in their category. And that 20 percent is further divided among every practice in a given market.

The implication is not that individual practices cannot be cited. It is that they cannot win by doing nothing. With 80 percent of the category occupied by two well-resourced entities, every remaining citation goes to practices that have actively built a visible, structured public presence. Passive practices get zero.

Understanding answer engine optimization as a discipline gives context for how practices can systematically build the kind of public presence that earns citations in the space pharma does not occupy.

Why Your Google Reviews Are Not Enough

Most dermatology practices invest in Google reviews because their marketing advisors told them it matters for local SEO. It does matter for local SEO. The problem is that AI platforms are not running local SEO algorithms.

ChatGPT, Perplexity, and Gemini each pull from different source sets. Google AI Overviews draw heavily from Google Business Profile data. Perplexity has its own web crawl. ChatGPT synthesizes from a mix of training data and real-time retrieval from sources like Healthgrades, Vitals, Zocdoc, and RateMDs. Having 300 Google reviews is genuinely valuable, but it is insufficient if you have 12 reviews on Healthgrades and nothing on Vitals.

There is also the semantic richness problem. AI does not just count reviews. It reads them. A review that says "great experience, would recommend" contains almost no useful signal for AI. A review that says "Dr. Chen diagnosed my atopic dermatitis and my flares have been manageable ever since" contains condition name, outcome description, and provider attribution. That review does meaningful work in AI retrieval. The first one does almost none.

What Dermatology AI Visibility Actually Looks Like

The gap between an AI-visible dermatology practice and an AI-invisible one is not a matter of quality of care or years in practice. It is a matter of how clearly and consistently the practice communicates its identity and expertise across the public sources AI indexes. The contrast is stark.

Note that advertising spend does not appear anywhere in this comparison. AI does not read ad budgets. The practices that capture the remaining 20 percent of dermatology citation space are earning it through consistency and clarity of public signals, not through paid channels.

Warning Signs Your Practice Is AI-Invisible

Most dermatology practices have never run a systematic check of their AI visibility. These are the indicators that suggest your practice is likely not appearing when patients ask AI for a recommendation.

If four or more of these apply to your practice, AI is almost certainly routing patients with skin concerns to your competitors. The channel is growing. Every month that passes without addressing these gaps is a month of patient referrals going somewhere else.

Frequently Asked Questions