Why AI Search Sometimes Recommends the Wrong Plastic Surgeon

Ryan Miller (00:00):
There's a real risk that if we don't become better fact checkers, better researchers using AI as a companion, not the decision maker, that we could in five years have a consumer base that is way more vulnerable than they are today.

Announcer (00:18):
You're listening to The La Jolla Cosmetic Podcast with Monique Ramsey.

Monique Ramsey (00:23):
You guys out there are probably familiar enough with ChatGPT or Claude, and you've probably already done it. You've typed a question into ChatGPT or asked your phone's AI assistant which plastic surgeon is best in your city. And it gave you a confident, detailed answer. But here's the thing. The answer might be built on a foundation of claims that no one ever verified. So today we're going to sit down with Ryan Miller, CEO of Aetna Interactive, a digital marketing agency that works with hundreds of plastic surgery practices across the country, including our own here at La Jolla Cosmetic Surgery. And Ryan recently spoke at the Aesthetic Society's annual meeting about something keeping a lot of people in this industry up at night. AI search has arrived with enormous power and almost no guardrails and some practices are already taking advantage of that. So it's not a scare tactic, it's a survival guide because the best way to protect yourself as a patient is to know exactly how this works and what to look for. So welcome, Ryan.

Ryan Miller (01:33):
Lovely to be here. Thanks for having me, Monique.

Monique Ramsey (01:35):
So you spoke at the Aesthetic Society about AI search and bad actors. And what did you see happening that made you feel that this needed to be said out loud at a major industry conference?

Ryan Miller (01:49):
Yeah. So at the start of 2026, as we were studying what was happening in the industry, which involves both looking at the results, the citations and recommendations that are coming out of AI search and the behaviors of the practices trying to influence those things, we saw this huge uptick in really two different kinds of bad actions. We saw lots of instances, both in the visible copy of the website as well as the code layers that are hidden underneath the site that are meant to be interpreted only by traditional and AI search engines, examples of doctors actively misleading patients about their standing in their medical specialty. And additionally, examples of practices filtering their reviews, suppressing the visibility of anything below a five star, suggesting that they were a practice with a perfect reputation when that really wasn't the case. And unfortunately what our studies were showing us was that bad actors, people who are engaging in these misleading tactics are being rewarded and patients are being put at risk, at risk of making choices off of data that's just fundamentally wrong.

Monique Ramsey (03:05):
For somebody who doesn't live in the AI digital marketing world, what is AI search and why is it different from just Googling something?

Ryan Miller (03:16):
Yeah. So I think maybe the way I want to answer this is to come at it two different ways. Let's first talk about the brands of AI search that we're probably almost familiar with that are going to key everyone in, all your listeners in very quickly. So this is Google's AI overview or Google's Gemini platform. It's ChatGPT, it's Cloud, it's Perplexity. But the other way we need to think about is what's materially different between a traditional Google organic search and today's AI search. In the old model, old model last year we would search, click, read, go back, click and read, go back, click and read. And a couple pages in, we'd start to synthesize our own answers. We'd start to decide what information we would hold onto, what information we would trust and begin to reach conclusions. The difference today with AI search is it's compressing that entire research cycle down and AI systems, AI search engines are returning a ready synthesized answer to the question in some ways sort of depriving or saving consumers the time to do all that clicking and reading on their own.

Monique Ramsey (04:24):
So if AI is telling a patient that a surgeon has the best results in the city, where is it pulling that from and how does it decide what's true?

Ryan Miller (04:35):
Where it pulls it from is pretty much everywhere that it can find on the web. So let's think about the information or the places where surgeons, their experience and their results are often discussed. So first and foremost, we have a practice's own website and AI systems we know place a lot of value in what surgeons, what practices say about themselves because they know that those practices are held to a fairly high standard in their communication. And every state in the nation, there are prohibitions against false, fraudulent, misleading advertising. So in theory, we all should be able to trust the information that's coming directly from a medical practice. But they're also looking at third party websites, rating and review websites, aesthetic industry specific websites like RealSelf is a popular one and community websites like Reddit. So synthesizing all this information. And importantly, I think you asked the question a very specific way.

(05:32):
So well, how are they deciding what's true? And the answer is they're not really. They're deciding what is most commonly or most often said about a practice or a doctor and they're simply repeating that answer.

Monique Ramsey (05:47):
So what are some of the most egregious unverified claims that you've seen practices put out there? And I'm not going to ask you to name names, but just thinking about AI regurgitating it back to somebody, repeating it as fact, what have you seen out there?

Ryan Miller (06:05):
Yeah. And it's a tough one for me because any misleading statement from a doctor to a patient feels egregious. There's not like a little lie that's okay and a bigger lie that somehow crosses a threshold to me. Any lie coming from a doctor fundamentally compromises, I think, trust and ultimately the credibility of the entire medical profession. And so let's just talk about what are the kinds of misleading information that's being put out there that could potentially cause a patient to make a decision with an incomplete or an inaccurate understanding of a doctor and their credentials. So the most common things that we see today are claims of superiority in a bunch of different forms. So a surgeon declaring themselves to be the best, either the best plastic surgeon, facial plastic surgeon, or the best at a specific procedure. In the San Diego market in particular, there's a rash of doctors who have on their website that they are ranked number one, but not explaining by whom or in what context.

(07:14):
And importantly, when we went looking, we couldn't find any scale in which they were ranked number one. And that information, when it's coming from a surgeon on the surgeon's site is given a lot of credibility by the systems because they know it's potentially punishable to publish misleading statements on your own site. So AI systems understandably place a lot of credence in that. The bigger one for me that I find especially disturbing is surgeons who grab all of the five star reviews from third party websites like Google, Vitals, RateMDs, Healthgrades, RealSelf, throw out the fours, the threes, the twos and the ones, collect them on their website and then broadcast to the world that these are all of our reviews. We only get five stars suggesting that there's a very particular type of coding that you can embed below the visible layer of the site called aggregate rating and declaring to the world they're a perfect five star practice when everywhere else you look, they're not.

(08:18):
And ironically, most of these practices have a more credible reputation presentation that's 4.8, 4.9, 4.7, which a lot of consumers value and understand that you can't always be perfect, but for some reason, whether it's an independent action on their marketing agency's part or it's something that they're deciding on personally out of insecurity, they're actually causing patients to, I think in many cases, question the credibility of the entire practice because you can go and look and see that you're not in fact a perfect five star practice everywhere else. But the AI engines see that and they look at that and they say, "Oh, this practice does in fact look exceptional."

Monique Ramsey (09:01):
It's interesting if you think about 30 years ago in this industry before the internet became a thing and you're thinking about patients having access to good information and it was hard for patients to access good information. Then the internet came along and everybody has access to all the information all the time. And they're more educated in a lot of ways than they ever were, like what board certification means and all these different things that they can read about or listen to a podcast and protect themselves. And so from a consumer point of view, like AI, you'd think, oh, that's natural outgrowth of this body of truth that it must be right. It's the smartest thing on the planet or off the planet. So is there a regulatory body or platform or algorithm that's actually checking whether these claims are true and who's supposed to be the referee?

Ryan Miller (09:59):
Yeah. It's a complex question because the challenge isn't that there is no regulatory body. In some ways, I think it's that there's too many. And so most consumers don't understand. So if you're a practicing physician in the United States, you're typically going to have several layers of regulation sitting over you. The most relevant I think is the state medical licensing board. So every state in the union has a medical licensing board that governs the issuance or the retraction of medical licenses for physicians and surgeons in the state. Then in different specialties, plastic surgery being a great example. There are at least one, sometimes two or more associations or organizations to which the surgeons belong that have additional layers of ethical codes and codes of conduct that they need to observe. In the case of some reputation issues, those are actually governed by the Federal Trade Commission.

(10:55):
So there's the possibility of Federal Trade Comission violations. The biggest challenge though isn't necessarily about the reporting bodies themselves, but the question that you asked about enforcement. So in order for enforcement to happen, somebody has to raise the issue. Somebody has to go to one of these bodies and formally file a complaint. In order for a complaint to start, you've got to go through this fairly arduous process of downloading PDFs, filling out a lengthy form about the nature of the violation, providing visual samples and examples. Many of these violations are actually hidden in code layers that the typical consumer wouldn't even know how to see. And in most cases, when we've actually pointed our partner clients and say, look, there's five of your competitors in your market who are breaking the law, who are doing things that are prohibited under state guidelines. Most of them have bowed out and said, well, I don't want to be the one to file this complaint because all of the complaints are public.

(11:56):
And you can, as a submitter, it's in the public domain. And you in some ways put a target on your own back when you point at a competitor and say, they're breaking the law. So what we find is that it's arduous to start a formal complaint. Practices are disincentivized to hold each other accountable because of this very public nature of the filing. And consumers often don't know enough about the letter of the law or the nature of the technical infractions to be able to accurately describe what's going on. It's a long way of getting to the fact that enforcement today is really not very likely to happen or happen effectively and consumers are going to remain at risk for the indefinite future.

Monique Ramsey (12:42):
Why would a practice decide to take this risk of making false claims in the age of AI? Walk us through the logic of that.

Ryan Miller (12:51):
Yeah. I think I want to be clear. I think in a lot of cases, what's happening is digital marketing agencies are looking at these tactics and recognize today they work. There's a really dark quote out there about if you repeat a lie often enough, it eventually becomes the truth. And right now the lies feel like little peckadiles. I feel like they're small things. You have a practice who's a 4.9, calling them a five. Is that really that big of a deal? You have a practice that's really pretty good and well established in the market. Is it really such a big deal to say they're the best in the market? And the answer in the medical marketing sphere is yes, those are lies. Anything that most misleads the consumer is a violation of the code. And we have a lot of times where we see where we're pretty confident that the doctor's not even aware of what the agency is doing on their behalf.

(13:45):
And so that's one body of these infractions. There are definitely those situations where surgeons are responsible for their own marketing and either unaware of those laws or just actively ignoring them. And right now they're doing it, Monique, to answer your question, because it works, because it is directly influencing how they're referenced inside of AI systems and these search results from AI search. And it's a pretty strong motive today given the rise in popularity of AI search.

Monique Ramsey (14:16):
What's crazy to me is how fast AI changes. You don't like the way the image came out today in three days. It's probably going to be perfect. I mean, it's kind of at a pace that's hard for us all to wrap our brain around. So how quickly can misinformation about a practice spread across any of the platforms once it's been seeded in there? And can you get it off? How do you have the AI unlearn something once it has learned it?

Ryan Miller (14:51):
So the short answer is hours. We've run tests where we've published information on a physician website and within 48 hours we've seen exact quotation of language that we've published on a site being regurgitated by an AI system. So it's fast. They're checking and they're rechecking the information that they have from crawled pages around the web fairly regularly. The getting it off is a harder question because if something gets repeated in multiple places around the web, it can be really hard because they are going to look at third party published information and they're going to marry it with what you say about yourself. So if you can't cleanse the third party sources, you may never get it taken down. Interestingly, we saw for one of the practices, we were asked to assess their AI visibility and the physician had been a part of a practice 16 years prior.

(15:47):
And that practice had been hit with a legal infraction for some off-label marketing of pharmaceutical products. To the best of our knowledge, that surgeon had never had any part in it. It was something that practice leadership had done. And 16 years later, AI systems were picking up that they had been a part of this practice that had had some legal concerns and it slapped it right back on top of them. So even distant information will follow a practitioner into the future.

Monique Ramsey (16:16):
That's crazy. So AI doesn't say maybe. Don't you feel like AI is super confident? Like yes, this is what you can believe in and this is what's true. So why is that? And why is confidence not the same as accuracy?

Ryan Miller (16:37):
I think it's important to understand that confidence is a user experience decision. It's a marketing decision. It's actually not real confidence. All of the AI search engines and AI systems in general are designed to feel helpful. And if you asked for a definitive answer and it came back, well, you might do this or you could do that, it wouldn't feel helpful and we wouldn't adopt it with such eagerness. And so recognizing that it is the way these systems work is it studies available information on the web. It comes up with a host of probable answers. And then the algorithm is designed to choose the probable answer that you as a human are most likely to accept, but nothing about it is most accurate. It's just most likely to pass the sniff test that you as a human being might aply to it. And so that confidence is a designed feature meant to convince you to keep coming back and using the systems over and over again.

Monique Ramsey (17:44):
Wow. So it's like it's the hook.

Ryan Miller (17:46):
It's the hook. It is absolutely the hook.

Monique Ramsey (17:48):
Right. So can you give us a real example that you might have seen where AI is sort of hallucinating when a patient is researching a plastic surgeon, what they might run into?

Ryan Miller (18:00):
So I think maybe we'll pause for a second and make sure your audience understand when we talk about hallucination in the context of AI, what that means. Hallucinations refer to AI systems making up information that sounds plausible based on available context. And so hallucinations happen when often there is incomplete data or there's similar data that confuses the AI system and causes it to just invent a really confident answer out of whole cloth. And we've seen AI systems recommend surgeons who are deceased or no longer practicing. That's a pretty common thing that comes up. We see AI systems positioning surgeons as an expert in a procedure that they don't even offer. We've seen AI systems endorsing a surgeon, but then placing them in a different practice and then recommending you contact them using a phone number that has no relationship to the surgeon that they're recommending whatsoever, basically accidentally steering you into a competing office.

(19:06):
And in most cases, you can bet those offices aren't going to say, oh, you've got the wrong number. They're going to try to help you and make you their patient. And importantly, we also see that they very often have to reach a conclusion about what a surgeon is known for. And when, especially in situations where you're asking for it to reach those conclusions about practices who have a very thin reputation profile, it is really good about making up information about what people in general think about it. And we've seen samples where there's two or three reviews and it just kind of makes some stuff up about the general reputation of the practice. One of the things I like to point out to our clients is there's no being forgiven once you've published it online. There are tools online, one in particular called the Wayback Machine, that basically has an image of your site, typically at least once a week, forever into the past.

(20:01):
And if a patient ever wanted to go back, even if you fixed it today, if it was present there just a couple of weeks ago, you can go and recall it and bring it to a court of law and say, "Yeah, but here's what they said three weeks ago." And so there's no hiding from it in the future.

Monique Ramsey (20:16):
Right. Now are ChatGPT Perplexity or Google Gemini overviews doing anything to vet those sources that they're pulling from? Or is it just that if it exists, we're going to assume it's correct and what's the current state of how it sources that information?

Ryan Miller (20:34):
Yeah. Let me put a really fine point on this one for your audience. Are any of the AI search engines vetting? Are they testing the accuracy of this data? The answer is no. None of them are. There is no protocol defined by them today to assert the accuracy. They're depending on those third party websites to do the work themselves to assure the accuracy of the data. Now it's not to say that the platforms... So Google is both an AI search engine, but it's also a ratings and review platform. And as recently as April 17th of this year, Google made a huge update to their policies around what's permissible in the solicitation of reviews. They're actually applying their own AI systems on top of the body of reviews to try to strip out things that look false specifically because one of the things we haven't talked about today is there are other AI systems, not just AI search.

(21:34):
And one of the applications that is popular is you can hire offshore companies to deploy AI software to fake reviews, to create false accounts, to add false activity on those accounts so they look like real human beings. And then to post what sound like real human reviews for businesses. And it's very inexpensive today to do this. And sites like Google who are also rating review sites are actively working to combat that. But are the AI search side of their business or perplexity doesn't have a review platform, Claude doesn't have a review platform. Are they doing anything beyond what the platform is doing to say these things are trustworthy and reliable? No. Importantly, what they're looking for is consensus or repetition. They're studying all the information and they're sort of leaning into the middle, the average. And the presumption is if it's said often enough, if it's repeated over and over again, it's probably close to something true and therefore safe to repeat in the form of a citation, which is a search result back to an AI searcher.

Monique Ramsey (22:40):
So that leads right into my next question. If you were advising a family member who's going to start doing their research on a surgeon and they're going to use AI as part of that search, what's the first thing that you would tell them to do or not to do?

Ryan Miller (22:55):
Yeah. Healthy skepticism is important here. So we have to understand that these AI systems, I think probably what we shared, we talked about just a bit ago is the most important thing. There is zero vetting. All it's doing is finding themes, the most commonly repeated information about a practice, but it doesn't have any way of knowing if the information that's been repeated is all true. So use the AI system to help you build lists, but then ask additional questions, go layers deeper in the way that you might have with a traditional organic search. So asking the question in one way, say you're considering a facelift surgery. Who are the leading practices for facelift surgery in your market like San Diego? Then once you get the results there, you can question it. Well, why did you choose this particular cohort of practice? And it will very often reveal to you both its internal bias and the limitations of the research that it did.

(23:47):
And then you can ask the question a different way. Who has the most robust collection of facelift before and after photos in the market? Or who has the largest number of reviews that specifically reference facelift surgery in them? And you can go at a different way, but it's this old idea of search, click, read, back, click, read, repeating some of those old behaviors because as a consumer, we still want you to be able to be empowered with a broad understanding and make a smart decision for yourself.

Monique Ramsey (24:18):
Along that line, if you asked the same question to ChatGPT, to Claude, to Grok, to whoever, to five different AI engines, would you get almost the same answer every time or would they be different? And is that something you would suggest doing, like asking different -

Ryan Miller (24:38):
Yeah, it's a great practice to switch platforms because there will be some differences. So when we originally started, it's now more than a year ago, we did a big study to try to understand which ratings and review platforms were having the biggest influence on each of the major AI search engines. And they are different and they're different by the design. And we can understand why. Let's just use ChatGPT as an example. ChatGPT is a direct competitor to Google. And so it doesn't make a lot of sense for ChatGPT to build their business model where they are entirely dependent on Google reviews. Yet Google is one of the places where practices tend to have the largest number of reviews. So ChatGPT, we did a study, hundreds of practices across the United States and found zero correlation between how well rated a practice was in Google and whether or not they earned a ChatGPT recommendation.

(25:27):
The correlation that we found was to a smaller aesthetic industry specific platform called RealSelf. And it made sense to us because RealSelf was focused on aesthetic medicine. So there was a body of reviews there. They tend to have longer reviews because they encourage their community of users to kind of tell more story than just write the review. And so it checked a lot of boxes and it kind of made sense to us that ChatGPT went that way. But here's the rub. A lot of our clients, the practice that we work with, have kind of walked back from really encouraging any of their patients to participate on RealSelf in the last maybe three to five years. And so it's a very slanted or very biased body of reviews on that site. So ChatGPT gives very different recommendations often from something like a Google AI overview. So there are natural differences between the various engines based on where they choose to prioritize and source data.

(26:22):
And consumers need to be aware that the recommendations are going to directly reflect that bias.

Monique Ramsey (26:29):
So what are the gold standard ways that you would tell somebody to how to verify the surgeon's credentials that no algorithm can fake?

Ryan Miller (26:40):
Yeah. So we go back to board certifications and I think that's a great starting place in terms of baseline credentials. If I have a medical concern or procedure I want to have, I want to find a specialist, someone whose training prepared them as well as possible to satisfy my need to provide the treatment I want. So general plastic surgery, breast, body, face, you're going to get board certified plastic surgeons. If you're considering a facial plastic surgery, you can safely bring into that group facial plastic surgeons, which are usually going to be otolaryngologists or ENT surgeons with certifications with specialty training layered on top of that. I always caution people to be mindful of cosmetic surgeons. These are individuals who have other surgical training and have often taken just weekend or shorter supplemental training courses to be able to perform specific procedures. It's not that you're guaranteed to have a bad result with a cosmetic surgeon, you're just guaranteed to have someone with less focused and specialized training.

(27:40):
In addition to that, once we move beyond the basic board certification, I really recommend a broad review of ratings and reviews. So you should look at summaries from an AI search engine. That's a great place to start, but then go look at the details on the platforms that matter most. And for aesthetic medicine, that's very often going to be platforms like Google. We're going to see real self factor into that mix because of its influence on ChatGPT. And then it's good to look at a smattering of other sites. You might see reviews on social sites like Facebook. You might find them on traditional medical rating and review sites like Healthgrades and Vitals and just look all across the board. The third layer for me though is really results. Spend as much time as you can with their before and after photos and look at them and say, do I like the results for myself?

(28:28):
Is it the kind of result that I would like? Because often in my experience, I've been at this now for almost 30 years, surgeons will have a particular style of outcome that they achieve, especially for breast procedures. And as a woman who's looking for a particular outcome, they may be an entirely competent surgeon who gets amazing results for the desires of one particular type of patient that may not be the thing that you want. So at least three layers of checking I think for me is a personal gold standard. It's not necessarily a magic bullet that you can use to get the results, to shortcut the research through AI.

Monique Ramsey (29:05):
Yeah. Now, do you have any questions that you would tell a patient you've got to ask? When you're in the room with the surgeon, what are those maybe key questions that you would ask them to help separate maybe how good they are, how ethical they are if they're telling the truth in some of their outward marketing?

Ryan Miller (29:29):
If you know they're lying in their marketing, you need to make a decision about whether or not you want the consultation at all. And it's an easy enough thing to see. If they're saying on their site they have a perfect five stars and you can go to Google and see they're a 4.7, for me, I wouldn't waste my time consulting that doctor because from the beginning, I know that trust isn't really something that I can place inside this practice. So I don't know that you need to punch somebody in the face with that question in the middle of a consultation. I would just not do the consultation. But I think there are at least three questions that when you you're live with a surgeon are really good to go a little bit deeper. The first one for me is specifically looking at case count. So on average, how many times do you do this procedure during the typical year and how does that compare to the national average for other surgeons?

(30:15):
So is this something that they do a lot of? I know for me, if I'm going to ask somebody to open me up, put me back together better than God made me, I want someone who does it a lot, who has a lot of experience because that means they've encountered a lot of differences in anatomy, a lot of potential risks and complications for outcome and they've worked their way through them. In addition to that, I want to know about specialized training. In addition to your basic training to arrive where you are professionally, was there any part of your formal training before you started practice or ongoing training that you've maintained to hone and refine your techniques? Surgical techniques evolve each and every year. There are new and better innovations that come out and you want to find someone who cares enough about their craft in that procedure that they both pursued specialized training early in their career and that they're maintaining that training going forward.

(31:06):
And then ultimately one of the things I encourage is really be open to the surgeons who say no. Because a lot of times we are aware that patients will go in with either an unrealistic expectation or maybe not even be the best candidate. And obviously there's a profit disincentive for a surgeon to say no to you. And so if they say no, I would caution consumers if the surgeon says this may not be the best choice for you to really listen to that doctor rather than just running to an office that hopefully says yes.

Monique Ramsey (31:41):
Yeah, exactly. Now, do you think ethical practices who have real reviews and real credentials and really nice outcomes, are they being hurt by all this noise and how do you advise them to sort of compete against whatever else going on out there?

Ryan Miller (32:00):
Yeah, for the ethical practice, they definitely are being harmed. We can find clear evidence today that these false claims are having a direct influence on AI citation. Our hope is that the AI systems will learn over time and do a better job and actually begin to install something that looks like vetting, something that looks like incredulity for hyperbolic claims of superiority that surgeons place on their own websites. The knowledge that if you're going to use the phrase best, just like the example that you gave, that you give the context that we were voted best in this particular popular contest in the community. And so those are things that don't exist today that we hope will exist in the near future. But I think ultimately in the absence of that, our advice is keep following your own ethical compass. I think business karma is a real thing and I do think there will be a price to pay for practices who are breaking the rules right now and not very distant future because as you mentioned, it's evolving so very quickly.

(33:12):
So hold to your compass, keep marketing ethically, keep doing all of the great things that you're doing and you will eventually be rewarded for it.

Monique Ramsey (33:21):
So five years from now, if we had a crystal ball or five minutes from now based on your frame of reference with AI, will patients do you think be more or less equipped to navigate how to search for a surgeon?

Ryan Miller (33:36):
Boy, it's a tough question. One of the things that is popularly discussed in the context of AI is the dumbing down of Americans. The fact that we are, it sounds so confident and it's so fast that it's quick and easy to defer both that the intellectual rigor that's required to make these important life decisions. We're handing off more and more creative responsibility to AI systems. So in one sense, there's a real risk that if we don't become better fact checkers, better researchers using AI as a companion, not the decision maker, that we could in five years have a consumer base that is way more vulnerable than they are today. I tend to be an optimist though, and I want to believe that in five years time, there'll be more instances where consumers have had first party experiences that AI recommendation didn't work out all that well and that a healthy skepticism develops that puts it in its place.

Monique Ramsey (34:38):
I think the patients out there, if you're listening, you can, like Ryan said, use it as a companion, but there's nothing that is going to substitute for looking at the before and after photos and seeing what you think about them. Do we like what that looks like? Calling the office, are they answering the phone? Do they leave you on hold? Did they take you seriously as a patient? Did they follow up? When you go to the office and you meet with the surgeon, were they friendly? Did they answer your questions? Did they take you seriously? Did the team seem warm and welcoming? Those feelings, AI can't give you that answer. And I think you've got to a lot of times go with your gut and don't turn off your internal feelings thinking, "Oh, well, AI is smarter than me, so it must be right." I think it comes down to it's a tool and it's a good tool, but since there aren't really any guardrails right now, it's a tool that you want to be using in a conscious way.

Ryan Miller (35:45):
I think the big thing for patients who are looking today in the near future for a surgeon provider is to remember the importance of fact checking. And that initial AI endorsement that you might get is a great start, but you need to extend your research just a little farther to make sure you see the broader context, understand the information that's influencing the AI decision, can make your own determination about whether the practice you're choosing is one that you can trust because plastic surgery is a deeply important decision that has lifelong consequences for the choice that you're going to make.

Monique Ramsey (36:21):
Well, thanks everybody for listening. I think this is one of our most important episodes right now because it has come on fast and furious. And whether you've been using AI for two days or two years, it changes so much that we want you all out there to know how to protect yourself and how to navigate through these treacherous waters sometimes. So look in the show notes. We'll have links to some of the things we talked about today. And thank you again, Ryan, for joining us and giving your professional advice and your deep expertise in this area. We really appreciate it.

Ryan Miller (37:03):
It's been a real pleasure, Monique. Thank you again.

Monique Ramsey (37:05):
Thank you everybody and we'll see you on the next one. Bye.

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