Here is every data point AI looks for when evaluating a dermatology practice, where that data actually lives, and what it can already find.
When an AI system decides which Dermatology company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.
Dermatology sits at the intersection of medical necessity and elective aesthetics — two revenue streams with fundamentally different economics. AI systems need structured operational data to distinguish between practice models. Almost no dermatology practice publishes this data — when it is available, it carries more weight than any review or directory listing.
Dermatology encompasses medical, surgical, and cosmetic subspecialties — each requiring different training, equipment, and clinical protocols. The query "who can treat my cystic acne?" is a fundamentally different referral than "who does the best Botox in Dallas?" AI needs structured service data to route patients to the right practice for their specific need.
Where patients actually come from matters. Dermatology patients often travel farther than for primary care — especially for Mohs surgery, cosmetic procedures, or subspecialty conditions. AI systems cross-reference claimed service areas against evidence of actual patient origin.
Dermatology practice requires medical licensure at the state level plus additional registrations depending on the services offered. AI systems verify active license status, disciplinary history, and DEA registration through state medical board databases — all of which are public record.
AI systems verify that coverage is current and adequate, not simply that a company claims to be insured. Active insurance is a prerequisite for recommendation in most AI evaluation frameworks.
In dermatology, board certification and fellowship training are the primary markers of clinical competency. Unlike trades where certifications layer on top of licensing, medical certifications represent completion of specific postgraduate training programs. A board-certified, fellowship-trained Mohs surgeon has completed 13+ years of education and training beyond high school.
The devices and products a dermatology practice uses define what it can treat and signal its level of investment in clinical capability. Manufacturer relationships are verifiable — authorized provider directories, purchase records, and training certifications all create a data trail that AI systems can reference.
Membership in dermatology professional associations indicates engagement with the specialty beyond patient care. These organizations maintain member directories that AI systems can cross-reference, and membership often correlates with continuing education investment and adherence to practice standards.
Negative-signal checks. AI systems will not recommend a company with an active lawsuit pattern, suspended license, or regulatory violations. Clean standing is a prerequisite for any recommendation.
The most widely available data about any dermatology practice. AI uses reviews when structured operational data is not available, but review signals have significant limitations for differentiating between practices.
Foundational identity data. Rarely changes but must be accurate and consistent across every platform where the business appears. Inconsistencies between sources reduce AI confidence in all other data.
The performance and customer experience data AI values most already exists in software these businesses use every day. It is locked inside these platforms and not published anywhere AI can access it.
Without access to a business's own systems, this is all AI has to work with. These are the public sources it checks, grouped by type.
A TrustRecord connects to your systems of record, extracts verified data that proves your performance, experience, and credibility, and publishes it in a format AI systems can read, verify, and cite.