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As most of our clients know, we’ve spent the past six months implementing marketing tactics designed to influence AI engines including Gemini, ChatGPT and Claude. We’re on the leading edge of what we’re calling AI optimization or “AIO,” and it’s been long enough now that we figured it would be time to assess what’s working and what still needs tinkering. Here’s an overview of what we found!
The Methodology
AI-assisted search has gone from emerging trend to daily behavior for a meaningful slice of the aesthetic patient population. When a prospective patient types ‘best plastic surgeon in [city]’ into ChatGPT or Gemini, does your practice show up? What about when they ask a follow-up question — ‘Is Dr. [Name] well rated?’ or ‘Should I choose Dr. A or Dr. B?’.
These are the key areas where we are trying to earn client mentions. So how do we know if it’s working? We conducted baseline audits in October 2025 and follow-up audits in April 2026 — six months apart — running the same five query types against three AI engines: ChatGPT, Claude, and Gemini. DeepSeek was included in the baseline but retired from the rotation after proving unreliable for entity recognition.
The five query types were consistent across every client:
- Unprompted best practices query (e.g., ‘Who are the best plastic surgeons in [City]?’)
- Named practice/doctor rating query (‘Is [Practice] well rated?’)
- Likes and dislikes query (‘What do people like and dislike about [Practice]?’)
- Head-to-head comparison query (‘Should I choose [Practice] or [Competitor]?’)
- Pricing/value query (‘Who has the best prices on tummy tucks in [City]?’)
We tracked results at the property level — which engine, which query, listed or not listed, position, framing, and change direction. The goal was to understand not just whether a client appeared, but how they were described and whether that description worked for or against a consultation decision.
Highlighting the Wins
1. One Client Achieved #1 Unprompted Across All Three Engines
This was the standout result of the entire audit cycle. For one plastic surgery practice, all three engines now list the practice at or near the top of the unprompted best-practices query — without anyone asking about it by name. ChatGPT describes it as ‘the clearest first stop.’ Claude surfaces it with specific competitive context against four named competitors. Gemini cites Castle Connolly Top Doctor status, Newsweek recognition, and names individual staff members.
2. Another Client Moved from #4 to #1 on ChatGPT
In our baseline audit, a cosmetic surgery practice appeared fourth on ChatGPT’s unprompted best-practices list for the market. Six months later, it’s first. In addition, a proprietary surgical technique the practice uses began appearing by name across all three AI engines, providing a unique differentiator that competitors can’t easily copy. When AI engines start citing your technique as a reason to choose you, you’ve crossed from findable into authoritative.
3. Clearing Up Competitor Confusion
One of our practices shared a name with a different doctor in a completely different specialty — a source of consistent AI confusion that was routing prospective patients to the wrong practice. By April 2026, all three engines correctly distinguished the two, describing our client accurately and without cross-contamination. This wasn’t a simple fix, but it was a high-value one: AI confusion about who a doctor is could kill a consultation before it starts.
What’s Working the Best
Getting uber-specific about credentials. Practices with detailed, specific credential information — board certifications, fellowship training, named awards, institutional affiliations, leadership roles — appear in AI responses with the supporting evidence. Practices with generic bio copy don’t.
Citing named techniques and differentiators. When a practice has a named approach — a specific surgical technique, a branded protocol, a recognized method — AI engines pick it up and use it as a differentiator. Bland positioning statements (“delivering natural results,” “offering personalized care”) don’t earn AI respect.
Earning more external citations. Practices getting cited by name have earned a reputation in external sources: press coverage, editorial roundups, directory profiles, awards pages, and more. On-site content has hit a limit. Off-site is where the remaining gains are.
Building review volume on medical-specific platforms. AI engines lean heavily on review volume as a proxy for trust. One of our clients lost the head-to-head recommendation to a competitor not because of quality, but because of a 9 vs. 112 review gap on Healthgrades. This is fixable, and it has a direct dollar value.
The Bottom Line
Six months of systematic work moved the needle in measurable ways for most of the practices in our AIO marketing plans. Some achieved best-in-market unprompted visibility. Others closed competitive gaps, corrected entity confusion, or built credential depth that AI engines are now citing in comparison queries.
The practices that didn’t move as much share a common characteristic: the remaining gains require off-site work — editorial coverage, directory citations, review volume on medical platforms — that takes longer to accumulate than on-site optimization.
