Ep 66 - AI in Orthodontics: From Research to Reality

In this episode of The Golden Age of Orthodontics Podcast, Dr. Leon Klempner and Amy Epstein are joined by Dr. Shankar Rengasamy Venugopalan, Chair of the Department of Orthodontics at Tufts University. Together, they discuss the transformative impact of AI on orthodontics and digital treatment plans. They’ll explore AI's role in treatment planning, emphasizing the need for transparency in AI algorithms and the importance of orthodontists' clinical judgment, as well as some of the limitations of AI. Dr. Venugopalan highlights the biological factors influencing tooth movement and reassures that AI cannot replace human expertise and patient-doctor relationship. Now is the time for orthodontists to embrace, rather than fear, technological advancements to enhance patient care through innovative approaches.

IN THIS EPISODE: 

• (3:30) Meet Dr. Venugopalan's background and expertise in orthodontics and AI

• (5:09) Precision medicine and its relevance in orthodontics

• (10:34) Dr. Venugopalan gives an overview of AI applications in diagnosis and treatment planning in orthodontics. Insights into the current state of AI research and its clinical applications.

• (19:12) The potential for integrating genomics and multi-modal data in orthodontic treatment. Plus, a discussion on the limitations and challenges associated with AI in orthodontics.

• (28:24) Will AI replace the need for orthodontists or serve as a supportive tool in treatment planning? 

• (33:26) It is important to consider the patient’s biology in treatment outcomes and the inherent limitations of AI tools in this regard

• (38:21) Dr. Venugopalan encourages the development of holistic clinical knowledge beyond just biomechanics in orthodontics and expresses hope for the integration of AI in improving patient outcomes.

KEY TAKEAWAYS: 

• AI is currently being applied in various aspects of clinical orthodontic practice, from diagnosis to treatment monitoring. These applications not only streamline clinical workflows but also enhance the quality of care provided to patients. 

• Despite its potential, AI in orthodontics faces several challenges and limitations. One major concern is the quality and diversity of data used to train AI models, as biased or incomplete datasets can lead to inaccurate predictions. Additionally, the integration of AI into clinical practice requires significant investment in technology and training, which may be a barrier for some practices.

• While technology offers numerous benefits, it is essential for orthodontists to maintain their clinical judgment and expertise. Technology should be viewed as a tool that enhances, rather than replaces, the orthodontist's skills and knowledge. As technology continues to advance, the role of orthodontists is evolving to encompass new skills and responsibilities. In the future, orthodontists will need to be proficient in digital tools and data analysis to effectively incorporate technology into their practice. They will also play a crucial role in interpreting AI-generated insights and integrating them into personalized treatment plans. 

• As the field becomes more tech-driven, orthodontists will have the opportunity to enhance patient care through innovative approaches, while also navigating the ethical and practical challenges that come with technological integration.

EPISODE TRANSCRIPT

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

Dr. Leon Klempner: (00:00:00) I don't have to tell you that cutting edge technology is reshaping healthcare as we know it, and orthodontics is no exception. How will AI transform our field? Could it even replace the role of the orthodontist? And what exciting possibilities lie ahead for the future of orthodontic care? Join us today while we explore these issues and much, much more.

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Take advantage of the new standard of care with 3D printed, fully customized brackets combined with indirect bonding and digital planning. You can learn a lot more and take advantage of some special offers by visiting our partner page at (00:01:00) pplpractice. com. 

Narrator: The future of orthodontics is evolving and changing every day, but although the way to achieve practice growth has changed, there's never been a better time to be an orthodontist.

Let's get into the minds of industry leaders, forward thinking orthodontists, and technology insiders to learn how they see the future of the orthodontic specialty. How will digital orthodontics, artificial intelligence, clear aligner therapy, remote monitoring, in house printing, and other innovations Change the way you practice.

Join your host, Dr. Leon Klempner and Amy Epstein each month as they bring you insights, tips, and guest interviews focused on helping you capitalize on the opportunities for practice growth. And now welcome to the Golden Age of Orthodontics with the co-founders of people and practice, Dr. Leon Klempner and Amy Epstein.

Dr. Leon Klempner: Welcome to the Golden Age of Orthodontics. I'm Leon Klempner, board certified retired orthodontist. (00:02:00) Proud graduate of the Tufts orthodontic program. Also the director of orthodontics at Mount Sinai hospital, part time faculty at Harvard, CEO of people in practice, all around really nice guy that cares about the profession.

And I'm joined by my lovely, bright, Intelligent, caring, empathetic daughter, Amy Epstein. 

Amy Epstein: I like the intros today. The intros today are superior to the intros normally. Thank you for that. Uh, thanks dad. I'm Amy Epstein. I have an MBA in marketing, 20 years of marketing and public relations experience. And about 10 years ago, we joined.

Together to start up people in practice, which is a digital marketing firm specifically focused on helping orthodontists better communicate what makes them different than their competition, (00:03:00) bringing to them ideas and new technology and how to market that new technology to their communities. So we help orthodontists to, um, To thrive in today's competitive marketplace and with all of the opportunities available through technology.

Speaking of which today we are very excited to well, so my dad mentioned that he's a tufts grad today. So that becomes particularly relevant. He's a jumbo. Um today we're very excited. Hey, 

Dr. Leon Klempner: hey, hey, hey, hey. 

Amy Epstein: I mean, not only are you the tallest person I know. I 

Dr. Leon Klempner: did put on a little weight, but come on. 

Amy Epstein: You're, you're also jumbo in other ways.

Your personality is jumbos. Okay. All those good things. Uh, but the jump, the Tufts university jumbos. So, uh, but, and today we're excited to welcome Dr. Shankar Rangasamy Venugopalan. I hope I did that. Thank you. Um, to the golden age of orthodontics, Dr. Shankar, (00:04:00) he's the chair and program director of orthodontics at Tufts university school of dental medicine.

He completed his dental training at the University of Missouri, Kansas City, earned his orthodontics certificate, and a DMSC in oral biology from Harvard and a PhD in biomedical science from Texas A& M. He is a specialist in genomics and craniofacial disorders, craniofacial development, mineralized tissue biology, and clinical orthodontics, and he's led significant research published widely.

And presented internationally, he's also served as the president of the craniofacial biology group and the International Association of Dental Research and has received numerous awards, no doubt. Um, and today of particular interest, um, in talking to Shankar is the, his understanding of AI as it relates to orthodontics from both.

An application standpoint and about its limitations. So we're really happy to have you here today. Shanker. Thank you for (00:05:00) joining us. 

Dr. Shankar Rengasamy Venugopalan: Thank you. Thank you, Amy, for your kind introduction and thank you, Leon, for having me here. I'm I'm excited, excited to be part of this podcast. 

Dr. Leon Klempner: Uh, well, we're excited to have you and, um, you know, uh, we hear about a lot of changes in healthcare and I want to kind of, like, start with kind of a more broader view because I know that.

Orthos in general have mixed feelings about AI and what's going to happen in the future. Some of them are threatened. Some of them are excited. And I do want to dig into some of that, but I first want to talk about just overall trends in healthcare in general. And I know that you've spoken before about, uh, the P4 paradigm in precision medicine.

And just for the benefit of our listeners, could you just explain exactly what that means and what that trend is showing us? 

Dr. Shankar Rengasamy Venugopalan: Absolutely. You know, (00:06:00) the idea of precision medicine has been around for a long time, right? So it's not new. You know, if you read the scientific literature, people have been talking about this for a great deal of time.

Um, it is becoming more and more Um, relevant these days based on, um, numerous clinical studies. I'll give you an example, right? So you go to a doctor, you have, uh, some type of a disease and the doctor is running a bunch of diagnostic testing and then come to a diagnosis or, uh, a clinical decision standpoint, and then.

Let's say the therapy involves, uh, prescribing certain drugs, right? Now, in Lucent, the, the dosage is sort of, uh, provided based on, on an average of, uh, a clinical trial, right? Whatever the dosage that was determined based on the group (00:07:00) of people, uh, uh, treated in a clinical trial, and whatever the outcome that was considered as, um, uh, favorable, and as long as that medication provided Or produced that outcome, then the doctor would be comfortable in saying that I'm giving you, you know, five milligrams of this is going to prevent whatnot.

So in the context of precision medicine, we think about four P's. The first P is predictive. So you want to have a technology that is highly predictive of a certain condition or a certain disorder. And then the second aspect, so it's not just enough it is predictive, but it also have to be preventive. So, it's, it's predicting at the right point where you can actually prevent the disease.

And then, moving forward, you are able to customize. So it's not like 15 milligrams for Shankar, same as 15 milligrams for Leon, 15 milligrams (00:08:00) for Amy, you know, our genetic makeup is different, um, as an individual, uh, each of us are different from each other. So it has to be really personalized. And then the last part is participatory.

So the P4 paradigm focuses on these, uh, four aspects. So, uh, think about it this way, how does it relate to orthodontics? 20 years ago, 30 years ago, people tried to develop a sort of a playbook for ortho, right? If it's class 2 division 1 malocclusion, 1, 2, 3, 4, this is how I'm going to treat it. But after practicing a number of years, you come to realize there are a lot more variables that are beyond our control.

Like, for example, growth. How much can you predict? Patient A is going to be a favorable grower versus patient B. So I put headgear on patient A, worked out great, the outcomes were great, and patient B may have been equally compliant as patient (00:09:00) A, but the inherent genetics puts a limitation in terms of what that desired outcome is going to be.

So, in the context of orthodontics, the predictive, the preventive, and then the personalized aspect has come a long way, and I think, uh, the P4 paradigm is not just relevant for our general medicine, but it is also very much relevant in the context of, um, orthodontics. Another example would be, a patient A develops severe external apical root resorption, Whereas patient B doesn't.

Why is that? There is a big genetic component to it. Individuals carrying genetic susceptibility for a certain amount of root resorption could range from mild to severe and you're applying the same level of forces to retract the anterior teeth in an extraction case and one responds with severe root resorption and other doesn't.

(00:10:00) So the P4 paradigm is sort of a framework in which how we think about Uh, precision medicine, and it has a lot of relevance in our specialty to the discipline of orthodontics. 

Amy Epstein: Well, that makes a ton of sense. Thanks for that, that, um, introduction and how it's relevant to orthodontics. So, um, how does AI then come in to support orthodontists who are looking to apply this, uh, P4 paradigm Um, you know, moving forward in the practice.

Dr. Shankar Rengasamy Venugopalan: Absolutely. So I can answer that question from a few different viewpoints, right? So what do we, what has been done so far in the context of AI in orthodontics? And, uh, what are some of the areas that still stay more in the research realm? Hasn't really translated into the clinical realm, but has a lot of potential (00:11:00) to go in that direction.

Thank you. And then we can also focus a little bit on, uh, what is that already in, uh, existence for, uh, clinical utilization. So, um, in the field of orthodontics, especially in the context of applications of AI, uh, a significant body of work has been done in the context of Diagnosis and treatment planning, right?

So, the most basic one is manual cephalometric analysis versus, uh, AI driven cephalometric analysis. Now, most orthodontists, after practicing for five years, they can look at a ceph and, uh, Make a, a skeletal diagnosis. They don't really have to, uh, draw the lines and measure the numbers. You know, the level of accuracy is within one to two degrees.

Um, but that's where most of the research work started in the context of, (00:12:00) uh, AI application and orthotics. Could we, the idea really is, could we develop a, uh, Uh, an algorithm where as soon as you push the button to take a lot of ceph, as it saves the image, you already have, uh, the cephalometrics done. So, and then the other area where a lot of work has been done is in the context of, um, um, treatment decision making.

So you have a certain set of patient comes to your office, let's say skeletal class 3. Now you have to make a determination. Let's say the patient has passed the age of, um, early intervention, and now you have to make a decision is this patient well within the range of, um, camouflage versus the patient really needs, uh, jaw surgery.

Not, not necessarily skeletal, uh, class three, it could be skeletal class two. So, in terms of treatment planning decision making, uh, what treatment (00:13:00) approach to take? uh, what teeth, uh, what, taking out what teeth would be most optimal for this particular patient. What type of surgery would be most optimal for this patient?

So, the benefit of this type of AI application is, so, you know, Leon has practiced for decades. So he may have come to a point where just by looking at the patient based on his experience, come up with a plan that might produce the most favorable outcome. Someone two, three years out of the clinical practice, we try to, you know, as an educator, we try to teach our residents an evidence based approach, but The clinical experience plays a big role, right?

And this is where the AI could come in as a, uh, as a supplement and say, Hey, I'm really toiling between, should I be taking teeth out on this case? Or should I be, um, not, right? And those are areas where AI could enhance or could serve (00:14:00) as an adjunct in the clinical decision making. And all this is still in the research realm.

Right. And I'm sure as we, uh, move through this podcast, we'll also talk about what are some of the limitations or what are some of the barriers that prevents translating this research into, uh, uh, a program that could be clinically utilized. And the other area is in prediction, you know, uh, friends of mine from University of, uh, Illinois, they published a paper, they've developed an AI based model system to, to predict cervical vertebrae maturation.

So, In the context of cervical vertebrae maturation, you put five orthodontists in the room, and they all come up with a different interpretation of the cervical vertebrae maturation stage, especially when the patient is right around in the, in the um, uh, So on the extremes, when (00:15:00) someone is too young or when someone is really matured, it's very easy to say, oh, the patient is young, still farther away from pubertal growth spurt, whereas someone who is an adult, it's easy to interpret.

Individuals who are in the middle, right? C3 and C4, you put a group of 10 orthodontists in the room, three will say C3, C4, and then some will say it's between C3 and C4. So, uh, my colleagues in the University of Illinois, uh, College of Dentistry, Dr. Mohamed Elnager and, uh, Dr. Saath El Oredi, they have published a paper, uh, utilizing AI model to make Uh, predictions of cervical vertebrae maturation, where some of these applications, as soon as you take a lateral sef, uh, if the AI algorithm could be integrated into our, um, patient management software, then it could, you know, before you walk to the station, you already have all those details readily available to you.

So (00:16:00) diagnosis, making treatment decisions, making predictions. These are all still in the realm of. Research. And, uh, there are a few things that are already utilized in the, in the clinical arena. The big one is, I'm sure most of you would have heard about this, the remote dental monitoring, which is an AI based application where you can monitor the treatment progress.

You take CDs of images and you can compare one time point versus another time point and make interpretations as to how well the treatment is, um, You Progressing and this has a lot of benefits as as as one could realize the patient, you know, the patient may not have to visit every so often as they would otherwise it could limit the number of times the patient actually visiting the provider as long as they are making progress with their.

In this context, uh, clear line of therapy, right? And there are two other AI platforms. The Overjet is a company (00:17:00) where, you know, you take a pantomograph and they can scan the pantomograph for all the dental diseases, caries, restoration, bone loss, and whatnot. There is another company which has already been in the market is Dent AI, which is more to do with period charting.

And this becomes particularly relevant in the context of, um, more and more orthodontists are treating adult patients. Um, and this, uh, AI application also has an ability to type in notes and whatnot. So, If you look at the applications of AI broadly in the context of orthodontics, like I said before, some are in the research and development phase, mostly focused on diagnosis, treatment planning, predicting, and what is right now in the clinical arena is monitoring treatment progress.

with remote tunnel monitoring, or interpreting images like, um, overjet, or, uh, specifically (00:18:00) looking into period issues like, um, dent AI. And I think the future has a lot of potential where all this could be combined. You know, what we do, you know, most of the research focuses on one aspect of the data, right?

It is very well established in medicine. Uh, like for example, If, for cephalometric tracing, you, you, you're mainly, uh, looking at one diagnostic modality of the many that is used by the orthodontist. The future of AI application really requires integrating multi modality of the data, multi dimensional data.

So you take the clinical evaluation, put that data into the system. You take pictures, put that data, diagnostic cast, uh, lateral Ceph, pantomograph, um, and all this multidimensional data could be used to come up with, uh, truly customized treatment (00:19:00) or make predictions in terms of, um, where this patient is heading, uh, In the context of growth, or what would be the best treatment modality for that particular patient.

Now, even further down the road, genomics could be integrated. Uh, as part of this, and, um, you know, with, uh, companies like 23andMe and there are, uh, several other enterprises where you can actually go and get your genome sequenced. So all that data is already out there. And, uh, a whole bunch of genes have already been established and associated with, um, skeletal class 2 malocclusion, class 3 malocclusion, enamel hyperplasia.

And, um, so scanning for. Some of this in the genetic makeup of the individual and say, like, for example, if someone carries a high risk variant or a high risk mutation for, um, external apical root resorption, right? And the way you might think about (00:20:00) treating that case with that information might be very different.

Then without that information, you may, you may not choose to do a treatment plan that requires more than 24 months. If the patient really requires extraction, you may decide to, you know, meet in the middle, uh, come up with a plan that is more, uh, addressing the patient's chief complaint. So I think, uh, I mean, I'm really excited about the future possibilities of, uh, AI, uh, in, in orthodontics.

You know, the potential is tremendous. 

Amy Epstein: So when we first brought you on, I mean, yes, truly, because, uh, imagine how precise you could get with all of those different variables and inputs and, uh, how much more effective treatment would be, how much shorter treatment would be, how many fewer issues we would have.

Um, You know, when I first introduced you, we talked about, um, applications. We also talked about your visibility into limitations. (00:21:00) Where do you see there are, um, challenges or limitations associated with A. I. In those particular applications that you mentioned, whether in the research realm or even in clinical practice right now.

Dr. Shankar Rengasamy Venugopalan: Absolutely. You know, it's anytime we think about a technology, right? If you don't understand its limitation, it could be, it could lead us into a direction that might not be fruitful for any of us. Right? So the 1st, um, big limitation is, um, from the context of our own research. So we We conducted a study when I was in the University of Iowa, the goal really was to take a pantomime graph and use that pantomime graph taken, let's say, right around the age of nine, eight, to predict whether the erupting canine is on a path towards a normal eruption or Is there a (00:22:00) risk for an impaction or impeded eruption, right?

So an pantomograph is taken, will look at the angulation of the canine, the amount of space available, uh, whether there is, uh, hyperplasia, like, you know, the jaw size is small, factor all that in, and make a decision, maybe we should intervene now, or maybe, Let's wait. So from that point of view, what we decided to do was we took a whole bunch of pantomographs of, of patients who had an outcome of an impacted canine.

And we also took a whole bunch of pantomographs where patients canine erupted normally without any intervention. So in the, uh, let's call the canine impaction group as the experimental group. And then the eruption as the control group. So within the experimental group, we had a series of pantomography (00:23:00) for that patient, like for example pantomograph taken at the age of 9, pantomograph taken at the age of let's say 11, and then at the age of 12, it was impacted, right?

So you have a sequence of images. Then what we did was we labeled the images in the experimental group as impacted canine, and we label the control group as normal canine. And then we used a, a program that was originally designed to identify, uh, the types of bird. If you took a picture of a bird, and you give it to the algorithm, and you don't know what that bird is, the algorithm will say this The picture that you submitted resembles more like a red robin, for example, right?

So we used that algorithm. And in that process, what we realized was, uh, we realized many things. There were two things that stood out. One, um, because these images were taken over the years, (00:24:00) they came out of different machines, and the sizes were different, right? Whatever images that was used in the training data set, like the image that you used to train the algorithm, and the level of accuracy, whatever we reached, and then when we moved on to a testing data set with images produced from a different machine, it fell apart.

So that's one big limitation. So if you think about it, I've developed an AI model system for an application in orthodontics, and then it's not be all and end all. So there has to be a way where you're constantly training this model to keep its accuracy really high. If that part of it was not taken care of, it could be leading us into an erroneous conclusion, and Unnecessary treatments or even missed opportunities, right?

And then the second aspect of it was, uh, what we call as a black box. Meaning, so you have (00:25:00) labeled two sets of images. One has impacted canine, the other one has a normally erupted canine. And submitted to this AI based algorithm. And we have developed a model and we have reached a certain accuracy where if you submit an unlabeled image, it will make a prediction.

This pantomime graph. will have a canine that is going to erupt normally, right? Now, when you ask an orthodontist, how did you come to that decision? The orthodontist will say, well, there is enough space, um, in the arch for that canine to erupt. The angulation of the canine is straight. Therefore, uh, the canine is going to be on the normal path of eruption.

But what we realized was the The algorithm was looking at something random in the corner of the Panama graph to come up with a decision that happened to be the right decision. So you really have to (00:26:00) train the model system to look at the correct, uh, aspects to make the accurate prediction right. So in this case, we were actively looking.

So, okay, so we, we have trained a model system where it is making correct predictions 70 percent of the time, let's say, or 80 percent of the time, or even 90 percent of the time. It is important that the end user must know how did the algorithm Come up with that decision, right? So if you don't know that aspect of it, and it becomes really a challenge when the algorithm is marketed by a company, it becomes a proprietary uh, aspect of that company.

They may not share that information. So which means then the company has to at least provide some type of a data to convince you that it has very high levels of accuracy, and then they are constantly training the model system to stay. Relevant and accurate. 

Dr. Leon Klempner: Let's talk a little bit about that for a (00:27:00) second and bring it, bring it to like a real world situation.

Um, it's clear to me like software AI software to determine cephalometric points or CVM are. Good tools to help orthodontists make good decisions. And the data to collect that seems to be pretty straightforward. But now when we, we talk about treatment planning, for example, now it becomes a little cloudier and, um, the more data that you have, the purity of your result is going to be, and currently I would say that the leading aligner company.

Has the most data regarding pre and post treatment and how we got there and my, my fear, frankly, is that a corporation could now hold that data because it's proprietary to (00:28:00) them and sell that, let's say, to, to orthodontists, for example, uh, submitting a treatment plan and then subscribing or something to, to this, this, uh, You know, uh, uh, corporation that would now tell you that, you know, your treatment plan has a You know, 85 percent chance of success.

But based on our data, if you click here and make these changes to your treatment plan, you will have a 96 percent chance of success. So almost like, uh, uh, eliminating the orthodontist in terms of the, the The most important aspect of our job, which is the the diagnosis and the treatment plan. Am I off base on this?

Or is this a concern? 

Dr. Shankar Rengasamy Venugopalan: See, I think what you what you said is 100 percent correct and accurate. Now, the way I see it is it's as a possibility, (00:29:00) and it doesn't actually scare me as much as, uh, some of my colleagues in the field, because. At the end of the day, AI application, so if something goes wrong from the liability standpoint, now who is going, who is, who is taking that liability?

It's the, the doctor who has established patient doctor relationship with that patient, right? So from that sense, the AI could be making predictions or AI could be making suggestions. And I don't think we have to be worried about in the context of AI is going to be replacing the. Uh, orthodontist. So at the end of the day, the way the tooth moves, there is a biological component associated with it.

So it's not like you go to the Google doctor and say, you know, the list of symptoms and then the Google doctor AI is now going to prescribe you a medicine, right? It's (00:30:00) not like that. It's more, it's more, it's, it's the AI application, especially in the context of aligner therapy or even treatment decision making, um, it could, it could very well act as a, as a adjunct or a supplement to make your outcomes better.

Um, Will it, uh, would, will it ever completely replace the orthodontist and you can go and sell stuff directly to, uh, to the patients? And I don't think our current, uh, legal system is structured that way. So the liability has to be assumed by someone, right? You cannot go and sue, uh, an algorithm if things went, didn't go well.

Uh, the way you want it to. So, um, instead of, um, you know, think about it this way, right? When, when, when we were treating with standard edgewise, orthodontists (00:31:00) were worried when straight wire appliance came about. Oh, now anybody can do orthodontics because all you have to do is just slide a series of wires.

And then it turned out to be that was not the case. Right, you, you very much needed an orthodontist to finish, uh, to an optimal, uh, occlusion. And then came, uh, the aligner, uh, therapy. And everybody was worried that, you know, I'm going to print this plastic material, put it on, it's going to move teeth, and the orthodontist will be eliminated in the picture.

And we all know most of the aligner therapy are only 60 percent accurate. For When it comes to final treatment outcome and then you have to revise and revise and revise until you get an optimal occlusion. So, when you bring AI into this, that 60 percent accuracy of the aligner is not due, not just because of lack of knowledge of the work of (00:32:00) honors.

It's more to do with the inherent limitation of The tool itself, when you're using plastic to move teeth versus the brackets and wires, which have evolved over many decades, has a much better, um, um, predictable treatment outcome as opposed to the inherent limitation of the plastic itself. So from that, from that, from these points of view, I think that, um, even, um, When AI is integrated into, uh, this leading Aligna company that you're talking about, or as a matter of fact, any Aligna company, you cannot completely eliminate the doctor from the picture.

So somebody, there has to be somebody watching over to make sure whatever the computer predicted is what happening in that patient's mouth. Now, the one thing that most of us, even though we (00:33:00) recognize, but don't acknowledge is the patient's biology, right? So, the patient's biology at the end of the day dictates how fast the teeth is going to move.

Um, and we are not factoring that as part of our AI application. So, the inherent limitation of the tool, the patient's biology, and then, uh, the various other factors that we talked about, Uh, we'll, we'll, we'll keep the doctor very much. So we don't have to be worried about that. And that worry should not prevent us from embracing a technology that could help us produce a desired outcome or a good outcome in an efficient way.

Dr. Leon Klempner: You know, um, I'm sure you get this question as well. If you're working with residents and some of the young practitioners will ask me, you know, did I make the right decision going into ortho? Because they're bombarded with a lot of this AI stuff and it's (00:34:00) threatening, understandably. Threatening and residents spend a lot of time, energy and money, um, uh, you know, attending, uh, you know, getting their training, et cetera.

And my, my response to them usually my almost always is that, um, That a I won't replace you, but your competitors that are leveraging a I in their practice just might, uh, meaning that, you know, it's important for us to be not afraid of the technology and see how we can use it to benefit our patients. I'm curious.

Have you ever gotten a resident that asked you that question? And and how did you respond to it 

Dr. Shankar Rengasamy Venugopalan: all the time? Um, So, you know, interestingly, orthodontics still, uh, happens to be the most, uh, soft doctor specialty. Every year we receive anywhere between, last year we received about 300 applications, and this year we received a little over 400 applications.

(00:35:00) For for nine spots, right? So that tells you the profession is still very competitive. And then the people get into this profession. If you're selecting nine people out of the 400, then you know, they are like, I mean, I jokingly tell my colleagues if I applied with these candidates, I don't know if I would get into an all residency program.

You 

Amy Epstein: know, application after application, these people seem like they couldn't possibly exist. They're so accomplished. 

Dr. Shankar Rengasamy Venugopalan: Exactly. So, um, and then when my residents ask me the, the, this is what I tell them, you know, technology, you, you, you, you cannot wish that the technology doesn't evolve. And then, you know, I use that as a safety net to protect your profession, right?

I'll be the most happiest person in the world if We could come up with a vaccine that would eliminate all the malocclusion, all the orthodontic problems. So our thought process should be in (00:36:00) that direction to evolve in a direction where we create a society, uh, of, um, healthy people and, uh, and, and, and place the wellbeing at the forefront.

Having said that. It's, you know, me being a researcher and a clinician, I do understand what it takes to to produce, to come up with the AI model system or any type of technology to be put into use into the clinic. So there is a long runway for us to get to a point where, you know, where you, everything is fully automated.

So we are in the beginning phase of AI. And there is a lot of opportunity for us to embrace it, incorporate it, and help, uh, this technology evolve and become better to serve our patients. And then the other aspect of it is, is I tell the residents that, uh, the idea of, Going through a (00:37:00) residency program to become an orthodontist is not just to learn the biomechanics of orthodontics, you know, 20, 30 years ago, orthodontics is entirely biomechanics, right?

So there was a lot of emphasis on biomechanics. Now, with the advent of TAD, some of the crazy complex movements that orthodontists thought would never be possible can be done now. So which means these tools and gadgets are going to help make your job better. Then the question is, what, what does it mean if I have to go through an orthodontic residency program to become a specialist?

So the idea really is to develop yourself as a clinician. You know, how do you take this knowledge of craniofacial growth and development, diagnosis, treatment planning, biomechanics, genetics, and how do you integrate all this knowledge to come up with a (00:38:00) plan, uh, that would efficiently produce an outcome, what the patient wants, and also promotes, uh, well being of the oral health.

So that should be the focus. You know, when residents get hung up too much on where do I position the bracket, yeah, that is important. But what is more important is you have to think about the patient as a whole, and that's where the P4 paradigm comes into the picture. 

Amy Epstein: Shankar, thank you so much for all this, uh, really enlightening information.

Um, you know, uh, all the detail you provided where, where sometimes, um, we're not in the research side of things. Um, and so it's really, uh, nice to hear what's on the horizon, uh, your perspective on it, what you're hearing from residents, what you're telling residents. So thank you so much for being here today.

Dr. Shankar Rengasamy Venugopalan: Well, thank you, Amy. That was, uh, this was wonderful. And, uh, thank you for the opportunity to, uh, you know, talk about this exciting stuff. And thank you, Leon, for the invitation. 

Dr. Leon Klempner: Oh, yeah, we appreciate you coming. (00:39:00) 

Amy Epstein: So if someone might have a question for you as a follow up to the AI, as a follow up to what it's like to be a student in the ortho program at Tufts, how can they reach you?

Dr. Shankar Rengasamy Venugopalan: So my email is my first name Shankar dot the first two initials of my last name or V or as in Romeo V as in Victor at Tufts. edu Shankar dot RV at Tufts. edu. 

Amy Epstein: Perfect. Thank you so much again. Appreciate you being here today and we hope to have you on again. 

Dr. Shankar Rengasamy Venugopalan: Well, thank you. Thank you, Amy. Thank you, Leon.

Thank you for the opportunity.

Amy Epstein: Please do tell a colleague. We appreciate that very much. For more information about people and practice, which is the, uh, marketing (00:40:00) firm that my dad and I founded together about 10 years ago, which helps orthodontic orthodontists in their clinical practice, promote themselves, differentiate themselves, attract new patients.

You can visit our website at pplpractice. com. 

Dr. Leon Klempner: Yep. Thanks for watching and listening. And if you don't already, you can't already tell, I mean, I enjoy doing this podcast and having knowledgeable people come on to talk about topics that are important to you. Uh, if you want to contact me directly, my email is Leon at PPL practice.

com. Uh, and today we talked about precision medicine and, and, uh, as it translates to orthodontics, customized, uh, appliances. Uh, our, one of our sponsors is light force. And if you would like some more, uh, information and a special offer, you can go to our partner page at PPL practice. com. Uh, we also have a sister podcast called practice talk that's designed (00:41:00) specifically for, uh, staff members talk about issues that come up in orthodontic practices all the time.

Uh, it's hosted by one of our team members, Lacey Ellis, and you can get that. Everywhere you get your podcasts as well. And most importantly, remember for forward thinking orthodontist, it has never been a better time to be an orthodontist. We are in the golden age, so take advantage of it. Bye for now.

Narrator: Thank you for tuning in to the golden age of orthodontics. Subscribe now on Apple podcasts, Spotify, or visit our website at the golden age of orthodontics. com for direct links to both the audio and video versions of this episode.