How to check what AI engines currently say about your neurosurgery practice

A regular self-check of what ChatGPT, Gemini, and Perplexity say about your neurosurgery practice reveals gaps between what these engines tell prospective patients and what is actually true today. Running the same handful of questions a patient might ask, then comparing the answers against your current services, credentials, and locations, shows you exactly where your online presence is helping you and where it is quietly costing you referrals.

Why a monthly self-check reveals your AI visibility gaps

AI engines answer patient questions by pulling together information from your website, directory listings, review platforms, and news mentions, then summarizing it into a single confident-sounding answer. If that source material is outdated, thin, or contradictory, the AI repeats the error with the same authority it would use for accurate information. A patient never sees the sourcing, only the answer, so mistakes go unnoticed until someone calls asking about a procedure you stopped offering or a surgeon who left the practice.

Checking this regularly matters because these engines update their answers as source material changes online, sometimes gradually and sometimes overnight. A practice that looked accurate in an AI answer a few months ago may now be missing a new surgeon, a new location, or a procedure that has since become a core service line. Building a short recurring habit of checking catches these shifts before they turn into lost consultations.

Questions to ask each engine about your specialty and location

The most useful check comes from asking the same practical questions a patient would type into ChatGPT, Gemini, or Perplexity, rather than generic questions about your practice name. Patients searching for spine and neurosurgery care tend to combine a symptom or procedure with a location, an insurance question, or a comparison between providers, so your test questions should mirror that pattern closely.

Useful prompts to run across each engine include: "Who are the best spine surgeons near your city?", "Does your practice name treat herniated discs without surgery?", "What insurance does your practice name accept?", "How do I get a consultation with a neurosurgeon in your city?", and "Compare your practice to your a nearby competitor for spinal fusion surgery." Ask each question on more than one engine, since ChatGPT, Gemini, and Perplexity draw from different sources and can produce noticeably different answers about the same practice.

Pay attention not just to whether your practice appears, but to how it is described. A patient deciding between a minimally invasive approach and traditional surgery is reading the AI's summary of your specialties as a factual account of what you offer, so any imprecision there directly shapes whether they call you or the practice down the road.

Spotting outdated or missing information in AI answers

Outdated or missing information tends to show up in a few recognizable patterns once you know what to look for: a surgeon who has left the practice still listed as active, a procedure your practice no longer performs still described as a specialty, an address or phone number that no longer matches your current location, or an insurance list that has not kept pace with your actual contracts. Each of these erodes patient trust the moment they discover the mismatch in person or on the phone.

The more damaging gap is a complete absence rather than an inaccuracy. If an AI engine cannot find enough information to answer a question about your practice at all, it either skips you entirely in favor of a competitor with clearer information, or it hedges with a vague, unhelpful answer that gives a patient no reason to choose you. Silence in an AI answer is functionally the same as a bad answer, because the patient moves on to whichever result gave them something concrete to act on.

Compare what the AI says against your own internal knowledge of the practice: current surgeon roster, current procedure list, current locations and hours, and current insurance relationships. Any place where the AI's answer diverges from that internal list is a finding worth writing down.

Recording what needs correcting on your site

Every discrepancy you find during a check is only useful if it gets written down in a way that connects it back to a fixable source. A simple running log works well: one line per finding, noting which engine gave the answer, what the answer said, what the correct information actually is, and where that correct information should live on your website or listings. Without this record, findings from one month's check get forgotten by the next.

Organize the log around a few practical groupings so it stays usable as it grows: factual errors that need correcting, such as a wrong address or a discontinued procedure still listed as active; missing information that needs adding, such as a new surgeon's credentials or a new location's hours; and unclear or thin information that needs expanding, such as a service page that mentions a procedure by name but gives an AI engine nothing substantial to summarize about it. Sorting findings this way makes it obvious which fixes are quick corrections and which require writing more complete content.

Keep the log dated. Because AI answers shift as source material online changes, a finding resolved this quarter can resurface later if a directory listing reverts or a review platform surfaces old information again. A dated log lets you see whether a fix actually held or needs revisiting.

Turning findings into a short action list

A log of discrepancies only creates value once it becomes a short, prioritized list of concrete fixes, ranked by how directly each one affects a patient's decision to call. An address error or an insurance mismatch belongs at the top, since those cause immediate friction for a patient trying to book. A thin service page describing a procedure in a single vague sentence belongs further down, since it is a slower-moving problem but still worth scheduling.

Assign each item on the list a clear next step: update the practice's Google Business Profile, correct a directory listing, revise a service page with more specific detail about the procedure and who it helps, or add a new surgeon's credentials to the team page. Because AI engines draw from many of the same sources patients already use, correcting information in one place often improves the accuracy of the answer across several engines at once, rather than requiring a separate fix for each one.

Set a recurring reminder to repeat the same set of questions on each engine after the fixes go live, and again on an ongoing basis afterward. This is less about a one-time cleanup and more about maintaining an accurate picture over time, since new surgeons join, procedures change, and competitors update their own information constantly. Treating the check as a recurring habit, done in whatever time it reasonably takes given the size of your practice, keeps the picture current instead of letting it drift again.

Which of your existing assets is already doing the most work for you

Before adding anything new, look at what you already have. Patient reviews mentioning specific procedures by name, photos showing your facility and equipment, FAQ sections answering real pre-surgical questions, and individual service pages for each procedure all feed directly into how AI engines describe your practice. To tell which asset is carrying the most weight, look at whether an AI engine's answer echoes specific language from that page or review; if it does, that asset is already shaping the AI's summary, and expanding similar content elsewhere is likely to help. If an AI answer ignores a page entirely despite it existing, that page needs more specific, concrete detail before it will influence what patients are told about your practice.