Veterinary Vertex
Veterinary Vertex is a weekly podcast that takes you behind the scenes of the clinical and research discoveries published in the Journal of the American Veterinary Medical Association (JAVMA) and the American Journal of Veterinary Research (AJVR). Tune in to learn about cutting-edge veterinary research and gain in-depth insights you won’t find anywhere else. Come away with knowledge you can put to use in your own practice – along with a healthy dose of inspiration to remind you what you love about veterinary medicine.
Veterinary Vertex
AI in Vet Med: From Bark to Bytes
Artificial intelligence is no longer the future of veterinary medicine—it's very much the present. In this captivating discussion with guest editors Casey Cazer, Parminder Basran, and Renata Ivanek, we explore the groundbreaking AJVR supplemental issue "From Bark to Bytes: Artificial Intelligence Transforming Veterinary Medicine."
The conversation reveals how AI applications already extend far beyond the clinical notes scribes that many practitioners might be familiar with. Veterinarians are now using AI-assisted stethoscopes to detect bovine respiratory disease, employing machine learning algorithms to predict Lyme disease risk patterns, and leveraging artificial intelligence to fill gaps in antimicrobial resistance surveillance data. Each application demonstrates how this technology can enhance clinical decision-making while accelerating vital research.
Our guests emphasize that successful AI implementation requires multidisciplinary collaboration, quality data, and thoughtful integration. "Garbage in, garbage out" remains a fundamental principle—without standardized, high-quality data, even the most sophisticated AI tools will produce unreliable results. The ethical dimensions of AI in veterinary medicine also take center stage in our discussion, from ensuring data privacy and informed consent to recognizing inherent biases and maintaining the veterinarian's ultimate responsibility for patient care.
For practitioners curious about incorporating AI tools into their workflow, our experts recommend starting with well-researched technologies, implementing them gradually, and evaluating how they affect the veterinarian-client relationship. As this field continues its rapid evolution, staying informed through resources like this supplemental issue becomes increasingly crucial for veterinarians who want to harness AI's potential while navigating its challenges. Join the conversation at the second Symposium for Artificial Intelligence in Veterinary Medicine at Cornell University (May 16-18, 2025) to explore how these technologies can help shape the future of animal healthcare.
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Sarah Wright:This is Veterinary Vertex, a podcast of the AVMA Journals. In this episode we chat about the March AJVR AI Supplemental Issue From Bark to Bytes Artificial Intelligence Transforming Veterinary Medicine, with our guests Casey Cazer, Parminder Basran and Renata Ivanek.
Lisa Fortier:Welcome everyone. I'm Editor-in-Chief Lisa Fortier, with co-host and Associate Editor Sarah Wright. Parminder and Casey welcome back. Renata thank you so much for being here with us today. Great to see my Cornell colleagues.
Renata Ivanek:Happy to be here, thank you.
Parminder Basran:Yes, absolutely.
Sarah Wright:All right, let's dive right in and talk all about AI. So, you are the guest editors for the AJVR supplemental issue. From Bark to Bytes Artificial Intelligence, transforming Veterinary Medicine. Casey, what can readers expect to take away from these articles?
Casey Cazer:In this special issue, I think readers will find papers written by different leading experts and researchers who are using AI in veterinary medicine.
Casey Cazer:These authors were actually presenters and attendees at the first annual Symposium of Artificial Intelligence in Veterinary Medicine that was held at Cornell University in April 2024. So, whether you are unsure what an LLM is or whether you're using AI tools every day in your veterinary practice, I think there's something new for everyone to learn in this special issue. I would encourage readers to start with the first two articles in the issue, which provide sort of a gentle introduction to AI in vet med and also the FDA's regulatory perspective on AI and machine learning in veterinary medicine, and the takeaways that I took from these two articles is that AI can be very useful right now or in the future in many aspects of veterinary medicine, including diagnostics, drug development, clinical decision making, record keeping and client communication. However, there are a lot of potential pitfalls, including a need for education of veterinarians and clients on these AI tools, a lack of high quality data to train the machine learning models, and ethical and trustworthiness challenges.
Lisa Fortier:Yeah, it was really fascinating. I enjoyed reading every one of the articles. I'm not honest, I didn't understand all of them, but I certainly learned so much reading them. Just the breadth of how AI is already involved in so much and what the potentials are. It's really fascinating. So thank you for the idea to form this supplemental issue.
Casey Cazer:Sure, so I hope that if people are interested in this topic, they want to consider attending the Symposium for Artificial Intelligence in Veterinary Medicine 2.0, which is going to be held at Cornell on May 16th to 18th 2025. And you can get more information on it at cornellaivet. org.
Lisa Fortier:Perfect, that's when our forsythia is in full bloom. So come on out to Ithaca. Hey, Parminder, what sparked your interest in artificial intelligence and then with your colleagues Casey and Renata, forming this symposium?
Parminder Basran:When I first joined Cornell about almost six years ago, there had already been a fair bit of research in human medicine and artificial intelligence that I was undertaking working in a human hospital and when I arrived here I realized that there wasn't nearly the same amount of push or, I guess, projects and the amount of resources that people are spending in veterinary medicine and artificial intelligence. So that to me really demonstrated sort of an unmet need needed within our environment here in veterinary medicine. So, there was a natural sort of progression of exploring artificial intelligence migrating from human medicine to veterinary medicine. And then soon after that, it's amazing how long ago two years feels from now. But if we were to go back in a time machine and think about what life was like two years ago, there were tremendous questions that we were asking ourselves about artificial intelligence and veterinary medicine. You know, things like ChatGPT weren't even on the radar and so at that time a bunch of us got together and said, hey it would be great if we could create an environment where we can bring together people from different disciplines in human medicine and in epidemiology and in companion animal medicine and computing science and engineers and just really talk about what artificial intelligence means in this space, and so that was the germ of the idea is for us to really just start having a conversation about this a couple of two years ago, and what happened after that was we were able to secure some funding through the NIH and the FDA to sponsor the event, brought together sort of these four pillars of veterinary medicine companion animal, population medicine, comparative health basically bringing together different components of artificial intelligence and veterinary medicine in a single setting.
Parminder Basran:We also realized at the time that there weren't a lot of avenues for our trainees to get together and talk about artificial intelligence in veterinary medicine.
Parminder Basran:There may be a lot of specialized AI conferences, but there wasn't really anything that really captured veterinary medicine. So again, trying to see if we could create a safe space for people of different disciplines to get together and talk about these important things was really important. And so having that funding from the FDA and the NIH helped really push the symposium along and we were really happy about that. And we were very pleased that we helped kind of break down some of the barriers for knowledge and education in that process as well. So you know, the more that we can share knowledge about things that we don't really understand, the less you know, the less fearful we are of that technology, and so we were pretty happy that we were able to do this thing and, based on our success of that event, it's really evolved and become something of a beast of itself, so they're pretty happy about that.
Lisa Fortier:Yep. Success comes with some responsibility, then, doesn't it? Yeah, walking around at the posters and reading about the speakers, how diverse their backgrounds were in coming and their disciplines, and how it's the most collaborative field of all it was fascinating.
Lisa Fortier:Well done to get that sort of attention as well on your first one!
Parminder Basran:I think that's a great point in the sense that the thing that we know about artificial intelligence and having successful practical applications of artificial intelligence is that it can't just be done in a silo. A single person can't do, can't get success, uh by by, you know, going alone. So so bringing people with different expertise and disciplines together was a big part of what we wanted to do, and indeed that really holds up in terms of the literature, in terms of the successfulness of AI adoption and implementation in clinical practices, is that those environments where that have lots of multidisciplinary teams, people from different backgrounds, tend to do very well in deploying AI in their communities and environments.
Lisa Fortier:That's fabulous, Casey. Back to you for a minute. Sarah asked you a little earlier what were some of the interesting take-home messages for folks, but always, at least for me, there was lots of surprises, both at the symposium and in things I just didn't know. What things surprised you in the supplemental issue, what did you learn that was like huh, I didn't know that?
Casey Cazer:Well, I think, actually similar to you and, how you said, walking around the symposium and looking at the posters, you were surprised at how much diversity there was. I think that in the supplemental issue I was surprised at the diversity of AI applications that are described. So you know, many veterinarians might be familiar with things like AI scribes, which are helped to complete your clinical note, and in fact Cornell just started using them recently as well. But we may not realize that AI is also used in diagnostic tests or diagnostic algorithms. So, for example, there's a paper describing AI-assisted stethoscopes and motion sensors to detect bovine respiratory disease.
Casey Cazer:Veterinarians may also not know how machine learning algorithms are being used for prediction, so for example, predicting the risk of specific diseases in cats or predicting the risk of Lyme disease, and then how we can use that information to help target our preventive measures or our diagnostic testing. And then I think also what's surprising is that these applications are not just used like on the clinic floor, but also to really accelerate veterinary research. So, for example, there's a paper from my lab about how we can fill in missing data from national antimicrobial resistance surveillance data sets using machine learning methods. And if we can accelerate veterinary research, we'll have a lot more exciting things to share and improve our veterinary medicine. So I think that you know, regardless of what your interest is, all of your AJVR readers will hopefully find an AI application that they think is relevant to them in that special issue.
Lisa Fortier:Yeah, fantastic. And to the readers and listeners it's open access, so anybody can read it anywhere. You don't have to subscribe. Sarah and I, weekly on this podcast, we ask the people that we're interviewing, who are always authors on manuscripts do you see a role for AI or machine learning in your area? And all of them come up with some fascinating responses. We recently had someone talking about ice baths for horses in the development phase of laminitis and they were like, yes, putting all this research together and all of these clinical parameters, so maybe we can noodle on that together as a team. And how can we come up with those, Because those are clinicians and scientists and how can we get them involved in these multidisciplinary teams as well? So maybe we can, Sarah and I can harvest all those responses and share them at your next symposium for people to find other clinical areas to harness, because that data is out there.
Casey Cazer:That would be really exciting because you're right, like we need different collaborations to come together to bring together the data and the skills and the expertise to make these things happen.
Renata Ivanek:And I would add to that we also need problems that are worth solving, or problems where there is strong demand to be solved.
Lisa Fortier:You're on it Sarah.
Sarah Wright:I was going to say, I see a spreadsheet forming in my mind right now, so stay tuned after this for that, but I was actually just reading too recently they're even looking at AI to help, like, diagnose certain diseases in corals, which is like obviously a really big deal, especially in the Caribbean right now. So, it's fascinating how much it can do. For those of you just joining us, we're discussing the AJVR AI supplemental issue with our guests Casey Parminder and Renata. So, Renata, what are the next steps for research in AI veterinary medicine?
Renata Ivanek:Oh, I think we are into for a lot, a lot more exciting discoveries, and I'll talk just for about a few that are within my own little circle of research. So I think there will be a lot of new developments in development of data systems that are able to retrieve and store and maybe share in a privacy-protected manner, data, because data and also standardization of that data, because if data AI needs a lot of data and if data is not standardized, if this practitioner and this practitioner are using different words, different criteria for diagnosis, then it absolutely makes no sense to make any insights. It doesn't matter how capable that AI is, the results will be completely meaningless. Another area where I think there will be a lot of movement will be predictive analytics, in case you already mentioned that a little bit. So, because we are very interested in using AI to predict disease progression or disease outcomes or patient outcomes, so that we can identify individuals that are at higher risk of developing disease, for example diabetes, to start to treat them earlier or maybe slow down progression of disease so that the disease doesn't actually even develop fully. For example, in my lab, we are very interested in infectious disease dynamics and how we can predict infection spread. So, in all times we would handcraft equations to describe the dynamics and then we will study them. That takes a lot of effort, expertise and synthesis a lot of information. Now we are using, or we want to use, AI to learn infection dynamics directly from the data. Another area that is of interest how do you predict where is threat? And then again, can you use some pattern of movement or pattern of sensors? We are, for example, developing a new technology that will predict where is contamination in complex environments like healthcare settings more likely Contamination, for example, with nosocomial pathogens and so trying to make that a little more efficient.
Renata Ivanek:Another area where I think there will be a lot of movement and we actually have already seen movement is surveillance and monitoring, for example, on farms. Nowadays it's becoming more and more common that farmers and veterinarians can monitor animals, thanks to wearables and different sensors, remotely, and they can get alerts when an animal is in distress or needs help. And because AI technology is able to take data from various resources, which can include veterinary records, but a lot more data, all kinds of sensors or cameras or audio records to find patterns and provide us with early warning systems. So, for example, in our research we are developing an AI-supported tool for antimicrobial stewardship in livestock that would provide farmers with insights to improve their business and animal health and welfare, while at the same time securing that we can continue using antimicrobials for the future. So a lot more is coming. It's a really really, really dynamic field. Right now, it's kind of quite exciting.
Lisa Fortier:Yeah, thanks for all those great examples. Renata, if you had advice to give to a veterinarian or a veterinary student who's interested in learning more about the intersection of AI and veterinary medicine, what advice would you give them?
Renata Ivanek:Yeah, I think a big one is about data quality. So we already alluded to this, and the old saying garbage in garbage out definitely holds for AI technologies. And so I would say, if you are a practitioner or you have your future practitioner, think, try to understand what kind of data are used to train or even to make prediction with this AI. How were data collected to make sure that AI insight is based on reliable information?
Renata Ivanek:Another one would be don't rush into this, into integration. If, when you are picking or when you are selecting air technology that you want to integrate into your practice or your everyday work, pick those first that there have a lot of research and testing behind them, and then, when you start using them again, don't rush. Give yourself time to learn how to use them to full potential and make mistakes. Make mistakes in a safe space before you integrate fully into their use. Also, so this AI.
Renata Ivanek:Why we would use AI? Because they would make our lives better and also the disease outcomes better. So we definitely see reason to use them good rationale but at what cost? And for example and I'm not talking about monetary costs, I'm talking about what would that do to relationship, for example, your relationship with the client, and then find maybe one other piece of advice Don't force it.
Renata Ivanek:If you are using, if you see an AI technology and it just doesn't sit right with you, it's quite likely others, other practitioners, will not like it either. And just wait, Market will provide either the same technology, new features or a better technology that will fit exactly what your needs are. And then, finally this is really as we already alluded this is such a fast-paced field, and being informed will be important and difficult at the same time, and so keeping up with new advancements, both in AI and veterinary medicine, will be really important, and we can do that, All of us can do that. Journals, conferences, we can take online courses just to be able to better understand how we can use this technology. These are all good resources, and even this podcast and this special issue is a step in the right direction to keep informed and open-minded.
Sarah Wright:Yeah, very good advice, thank you. Now, this next question is definitely one of our more challenging questions. If you can take all the information that's in this supplemental issue and boil it down to one really important nugget of take-home information that's going to help veterinarians, what should they know? So, casey, what is one piece of information the veterinarian should know about the AJVR-AI supplemental issue?
Casey Cazer:That is really difficult and I'm going to kind of tack on to what Renata was saying about good data.
Casey Cazer:So we need veterinarians to realize that to have high quality AI tools and useful AI tools, you need a lot of high quality data.
Casey Cazer:Everything in your practice management software or your farm management software, there's a lot of information, but that data is often either low quality or isolated and difficult to connect to other bits of information. And, as Renata said, there's a saying garbage in, garbage out. So if you don't have good data, you're not going to get good results. So what I think veterinarians can actually do quite a lot to improve data quality and therefore have better AI tools. So, for example, many of the new AI tools for small animal veterinarians are being built from clinical notes right, the information that we put in about the patients that we see, and so we can make sure that your clinical notes are accurate and complete, including useful things like either master problem lists or diagnosis codes, so that those AI tools can use that to identify relevant cases. And in fact, there's a little bit of an AI loop here where you can use an AI scribe to help write your clinical note that's listening to your appointment and therefore write a better and more complete clinical note.
Sarah Wright:And the other side of the relationship. What's one thing that clients should know about this important supplemental issue?
Parminder Basran:Well, I'm going to defer to you, Renata, about what that students should probably know about this.
Parminder Basran:But the one small thing that I would add in relation to things to take away from the supplemental issue is is this the breadth and scope of the kinds of things that are presented in the supplement itself demonstrates to me how big of a tent veterinary medicine is in general and how AI fits inside this giant tent of different specialties. And so, because of that, there's a unique advantage in veterinary medicine in the sense that it's easy to survey and essay the different adoptions of artificial intelligence in different disciplines and really think about how one might be able to transfer that kind of information or application into their domain, and that, I think, is a really exciting thing to have. Often, if you're a specialist within a specialty, you don't get a chance to see a lot of the different applications and different disciplines. So I think that's the one thing that I personally would take away from this is that it really is just an excellent example of how big the tent is and how much we can learn from each other.
Renata Ivanek:Okay, so I would piggyback on that and I would focus on ethics, and the reason for that is because ethics is actually still AI, ethics is still under development. We really still don't have guidelines or rules how we should apply mindfully AI technologies and we still actually don't even know what the full breadth of even benefits or dangers, everything that can go wrong. And so, as we are tapping into this new technology, I think we all have to take responsibility to think about AI applications, about what's ethical, what's moral. So a lot is more coming, but I think there are still some things that we know will hold like in any future guidelines.
Renata Ivanek:First, data privacy and security has to be preserved and it has to be clear whether the client provided informed consent that data could be used for AI. We know from tragedy, from hallucination we hear about, that AI system can have inherent biases, so we have to accept the possibility that AI could be wrong, it doesn't matter how sophisticated it could be wrong. Also, we have to be able to make an effort to understand how did AI come to a conclusion or decision, especially if that decision contradicts our own, and this is important to explain to ourselves what should we trust? But also to be able to explain that to clients. And then I think, maybe above all, veterinarians are top experts in this field, and so they should retain ultimate responsibility for patient care, even when AI is used.
Lisa Fortier:Yeah, really, really great points. Thank you guys again for being here. We learned a ton reading all the manuscripts, even more today, so thank you again for your time. Thank you.
Sarah Wright:Thank you.
Lisa Fortier:As we wind down, we like to ask a little bit of a fun question. So, Casey, we'll start with you. When you complete a puzzle, do you begin with the interior, middle or the exterior border pieces begin?
Casey Cazer:So, I actually I like to do puzzles, but I'll do them like over Christmas and that's it Cause otherwise I'm like a puzzle addict and I'll just like doing them. And I used to always start with the border and recently I've been starting with the middle, like I'll sort them and find some interesting feature in the middle and start with that Interesting.
Lisa Fortier:We haven't had a switcher yet. Our prediction is we're going to retrospectively look at this, but typically we find that surgeon types do the exterior and more medicine-leaning people do the interior. So maybe your community practice is getting to you.
Casey Cazer:Maybe you could build an AIM-I model to see if you can predict that feature.
Lisa Fortier:Okay, back to you, Renata. If you could have a superpower, what would it be, and why?
Renata Ivanek:I want to be a healer. I feel it's like a catch-all. I could then treat and save everybody around me, whether they're and me included, and maybe what helps with living long and happy life.
Lisa Fortier:Good, Best politics start at home, so heal yourself first. Parminder what is the oldest or the most interesting thing on your desk or in your desk drawer?
Parminder Basran:Well, I was going to say something you know cheesy, like my savvy mug, um uh, just to just to promote that. But I have what I write, what I have on my hand. I'm sorry it's not really all in focus, but this is a piece of carbon. Do you remember the show oppenheimer?
Parminder Basran:yes you remember there was a scene in a basement in the university of chicago where they were testing for the very first time a controlled nuclear fusion or fission. I don't remember the scene, but I remember the book. There was a scene down there. So the very first nuclear reaction. What took place underneath the University of Chicago? It was all contained with large slabs of carbon to help shield the radiation. This is an actual slab from the Pio-1 nuclear reactor. So this is a very nerdy thing. I love this thing and I brag about it to my PhD supervisor every time I see him.
Lisa Fortier:That is super cool. I'm a trickster, so if I got into your office I would make a fake piece and see how long it took you to notice.
Parminder Basran:I've got lots of really weird things in my office. I've got Lego pieces and it's a. It's a I love. I love collecting odd weird things.
Casey Cazer:So 2.0, the Symposium on Artificial Intelligence and Veterinary Medicine. It's going to happen at Cornell University, may 16th to the 18th 2025. You can get more information at cornelaivetorg. We're going to have some fantastic keynotes about wildlife medicine, livestock medicine and companion animal medicine, and a little bit on human health and how AI is influencing all of those areas.
Sarah Wright:And to our listeners. We'll have the link to that website as part of the description of this episode, so if you scroll down, you can find it. Well, thank you so much again. Parminder, casey and Renata Really appreciate you being here today serving as guest editors for this supplemental issue and just sharing insights about it to our listeners. Thanks very much.
Parminder Basran:Thank you.
Sarah Wright:And again to our listeners. You can read the AI supplemental issue in AJVR. I'm Sarah Wright with Lisa Fortier. Be on the lookout for next week's episode and don't forget to leave us a rating and review on Apple Podcasts or whatever platform you listen to.
