The NYC Healthcare AI Search Landscape: Who's Winning and Why

Over a six-week period in early 2026, our team conducted a systematic analysis of AI search results for healthcare queries across New York City's five boroughs. We ran 50+ queries across ChatGPT, Gemini, and Perplexity, covering 12 medical specialties and all five boroughs. The findings were illuminating — and in some cases, surprising.

Methodology

We structured our queries to mirror how patients actually search for healthcare providers. Queries included:

• Specialty + location ("cardiologist in Brooklyn")
• Condition + location ("doctor for back pain in Queens")
• Procedure + location ("colonoscopy near me in Manhattan")
• Quality-seeking queries ("best orthopedic surgeon in the Bronx")
• Specific condition queries ("rheumatologist for lupus in Staten Island")

For each query, we recorded which practices were cited, how prominently they appeared, and what information was included in the citation. We then analyzed the cited practices to identify common characteristics.

Key Finding #1: Structured Data Is the Strongest Predictor of Citation

The single strongest predictor of AI citation across all three platforms was the presence of proper Schema.org markup on the practice's website. Practices with physician and medical organization schema were cited at 4.1x the rate of practices without it.

This finding held across specialties, boroughs, and AI platforms. Structured data appears to be the foundational requirement for consistent AI citation — without it, even practices with excellent reviews and strong content are frequently overlooked.

Key Finding #2: Review Velocity Matters More Than Total Volume

We expected practices with the most reviews to dominate AI citations. The reality was more nuanced. Review velocity — the rate of new review generation — was a stronger predictor of citation than total review count.

Practices with 50 total reviews but 15 in the past 90 days consistently outperformed practices with 200 total reviews but none in the past year. AI engines appear to interpret recent review activity as a signal of an active, relevant practice.

The sweet spot we identified: 4.5+ stars, minimum 30 total reviews, with at least 5 reviews in the past 90 days. Practices meeting all three criteria were cited in 73% of relevant queries. Practices meeting none were cited in fewer than 8%.

Key Finding #3: Directory Presence Is Non-Negotiable

Every practice cited in our analysis had a complete, accurate profile on at least three of the following platforms: Healthgrades, Zocdoc, Doximity, WebMD, US News Health, and Castle Connolly. Practices missing from these platforms were almost never cited, regardless of their website quality or review profile.

This finding underscores the importance of what we call "entity establishment" — ensuring that your practice is recognized as a distinct, trustworthy entity across the platforms that AI engines use as reference sources.

Key Finding #4: Content Depth Drives Specialty-Specific Citations

For condition-specific and procedure-specific queries, content depth was the decisive factor. Practices with detailed, patient-facing pages about specific conditions and procedures were cited for those queries at dramatically higher rates than practices with only general specialty pages.

A gastroenterology practice with a detailed "What to Expect During a Colonoscopy" page was cited for colonoscopy-related queries at 6.2x the rate of a gastroenterology practice with only a general services page. The content investment directly translated to citation frequency.

Key Finding #5: Geographic Patterns Are Stark

AI citation rates varied significantly across the five boroughs, reflecting the uneven distribution of practices that have invested in digital optimization.

Manhattan practices dominated AI citations across all specialties — not because Manhattan has better doctors, but because Manhattan practices have historically invested more in digital marketing and therefore have stronger online presences. Brooklyn and Queens showed the most opportunity: large patient populations, significant competition, but relatively few practices with optimized AI search profiles.

The Bronx and Staten Island showed the most dramatic opportunity gaps. In several specialties, we found that a single well-optimized practice dominated AI citations across the entire borough — because it was the only practice that had made any investment in AI search optimization.

Key Finding #6: The Winner-Take-Most Dynamic

Perhaps the most important finding from our analysis: AI search exhibits a winner-take-most dynamic that's more extreme than traditional search. In most queries, 1–3 practices captured the vast majority of citations. The long tail of practices received almost no AI visibility.

This dynamic has significant implications for practices considering when to invest in AI search optimization. The practices that establish citation authority now will be increasingly difficult to displace as AI search matures. The cost of waiting is not just missed citations today — it's the compounding disadvantage of starting later in a winner-take-most market.

Implications for NYC Healthcare Practices

The data tells a clear story: AI search visibility in NYC healthcare is currently determined by a small number of technical and content factors, most of which are within the control of any practice willing to invest in them. The practices winning today aren't winning because they're better doctors — they're winning because they've made their excellence visible to AI engines.

The window for establishing early-mover advantage is real and finite. Our analysis suggests that within 18–24 months, the AI citation landscape in NYC healthcare will be significantly more competitive and significantly harder to break into. The time to act is now.