Welcome, everybody. I'm so excited to finally be doing this session. I know we've been really thinking about this for a long time, and I am more than delighted to have this outstanding panel and excited to share some of their insights. I think that in recent years and months we've had lots of progress, and really the AI systems have been really catching up to human-level performance in many cognitive tasks. And it's very likely that AI is going to be one of the most revolutionary innovations for increasing and enhancing productivity in our day-to-day life, and it's going to have huge impact in our healthcare system specifically. I had the pleasure of working with my colleague, Dr. David McManus, and we shared and edited a series of articles that are published now in Heart Rhythm Journal that you can get access to if you just click on the link that's provided in the description of this session. And I'm delighted to have the authors of those articles here with me to discuss some of the key concepts from those articles. So without further delay, I want to go ahead and have them introduce themselves. Elaine. Hi. Good afternoon. I'm so excited to be here. My name is Dr. Elaine Awad. I'm an associate professor at Columbia University. I'm a physician-scientist, just like Hamid, and I'm an electrophysiologist, so I do procedures. I also run clinical trials at Columbia University and has been part of some national and international trials. So I'm so happy to share the stage with my colleagues. Kevin. So, good afternoon, everyone, and it's really exciting to be back here in Atlanta. I went to undergrad here at Emory, and so always good to come back to the city. And excited to be here with you all for now the third annual HRX conference. And so it's literally, I'm going to shameless plug here, it's one of the most amazing conferences that I've been to. I just think the breadth of people who come together I think is incredible, and you learn so much and have conversations that we don't have at our hardcore scientific sessions. And so it's really a joy to be here. So I'm a professor of medicine at Duke University. I'm an electrophysiologist. I'm also a vice dean for equity, diversity, and inclusion at Duke, and a health equity researcher. So kind of had a full portfolio of things. And so I'm excited to talk about AI, and I was sharing with someone earlier that I'm incredibly excited about AI and terrified all at the same time. And so hopefully we can get into some of that conversation today. Hello, everyone. My name is Chanho Lim, and I'm the assistant director of digital health at Tulane University, and I'm a machine learning engineer. And I'm really excited to be here and discuss the digital health evolution that's been happening around us today, and looking forward to this talk, yeah. Good afternoon, everybody. Honored and privileged to be here at this meeting and to be sitting next to an engineer. My father was an engineer, and I always like the way that engineers can take technology and ideas and translate them into reality. So I like this diverse group. You know, this is a great meeting, and it's different than our usual ones. And I think what I'd really like to ask all of you to do is make sure you interact with us either on the stage here during this presentation or catching us in the hallways, because by sharing thoughts, by sharing ideas, and by asking difficult questions, we get better. My name is Thomas Dearing, and I'm a Piedmont health care electrophysiologist. So I had an incredibly long drive, and as Jack and I were talking about, I'm totally worn out by the three-mile distance that I had to travel to get here. I've done a lot of work. I've run our arrhythmia section. I've also, you know, lead what we call now our cardiac governance group, which is the entire, you know, system of 25 hospitals. And I really lead a lot of value-based care within our health care system. And I think AI has an important role there. And I also like a different term for AI, augmented intelligence, not artificial intelligence. So now I'll hand it over to my colleague to the left, Jag. Hi, I'm Jag Singh. I just want to say that AI actually stands for actually Indian. I'm Jag Singh. I'm a cardiac electrophysiologist at Mass General Hospital, a physician scientist, and a professor of medicine at Howard Medical School. Just delighted to be here. I think we have a phenomenal panel out here. Just delighted to be sitting next to Tom and Blern on this side, and really excited about the conversation we're going to have. And hopefully we'll be able to tease apart many aspects of electrophysiology and AI, not just on stage, but even after we're off stage. Hi everyone. My name is Blern Baraliu. I'm CEO and founder at 91 Life. No jokes from me, unfortunately. I'm a mathematician by background, started working, studied pure math and sort of heard the sirens of Wall Street. So I did data science and AI and trading and derivatives and so on and so forth. But I always had envisioned a more meaningful purpose in my life. So about 13 years ago, and my wife was better than me. She had studied and is now an interventional cardiologist. So I started thinking about how we can apply math to medicine. It was very difficult. Nobody believed in the beginning. They all wanted those servers in the basement where they kept the patient data. And you know, actually that was an upgrade from the files in the cabinets. But then ultimately we found a way to get into data and I quickly realized that electrophysiology is at the forefront of what's going to drive innovation. So our dedication is to advance and contribute what we can to this digital health with, I agree with Tom, with augmented intelligence where we empower physicians with intelligence from big data and sort of other technological innovation. I'm honored and privileged to be in this tough group. So I'm a bit outside of my depth, but I'll try to hang on. Thank you. Thank you so much. What a wonderful group. And I'm excited to kind of start talking. If you have any questions, please put it into the chat and I'll be sure to have it post to our panelists. So I want to start with the first question. How can we use AI in clinical practice to improve our clinical decision making? And if you could maybe mention how you're actually using it in clinical practice right now and how it's helping you. And if you could also touch a little bit on like how you think that is changing how you're interacting with your patients and technology. Elaine, maybe. Well, I think for all of us electrophysiologists, probably digital health and AI came first for analysis for ECG. Because early on when we had ECGs and we had so many to read, sort of machine learning and improving diagnosis and of these electrocardiograms seemed the most obvious leap. And then for us as electrophysiologists, a lot of the mapping, that seemed to be another thing that was easy for it to be implemented for us. I think in the clinical side, we see a lot of the AI now for helping us for diagnosis and EMR to sort of shorten the time to figure out what patients need. And also helping in making sure that they get to the right specialists looking at the right charts, et cetera. But I think it's been brought in about how to implement all of this powerful technology. And I think one of the things that might be limiting is we have all this data, but then how are we going to bridge the gap from the physician using it to the patient? And then also this AI is designed for different operators. So for example, a lot of the different algorithms are how our doctor is going to use it. But then as we saw earlier, some of these companies is how our patient's going to use it. So I think the user interface should need to be specific on who's going to use it and how can we bring those two end operators together using these new technology. Yeah, I've been spending a lot of time really trying to understand it and figure out how I'm going to incorporate it into my practice, into my research. But really specifically about how it's going to improve patient care. Because I think we can very easily become intoxicated by the innovative things that it does. And it's amazing. There's no doubt about it. I use iterative AI all the time for writing emails and some practical things that I feel like have made me more efficient. I'm asked to write a lot of letters for promotions. I've learned how to incorporate it in that workflow. And that's, I think, really accomplished some important things. But I really want to challenge us to keep patients at the center of everything we do. Because that's what's going to be most important. You know, the early opportunities and how I've used it in my practice is I've used the virtual AI platform through Nuance and DAX to engage with patients and to take notes and new encounters, follow-up notes, and things like that. And it really has been pretty incredible. And it's made me faster. It's made me be able to focus more on the patient, be more present in the exam rooms. And so I think that's really important. The other part of that that I think that we have to consider is, you know, burnout is a real problem for clinicians. It's a real problem. And so again, as we think about AI, there's lots of questions that we can ask. There's lots of priorities we can have. One of those has got to be, how do we make wellness better for clinicians? Because the burnout rates are currently at an all-time high. And so thinking about how to integrate this into your practice in a very dogmatic, practical way is really important. And so as I've thought about how I'm going to use it, particularly in the early stages as we're still learning and iterating, incorporating it into, again, how I interact with patients, how I can get my notes done, and having that workflow be more seamless is how I've interacted with it. It's a little difficult for me to speak on how it changed my practice as an engineer. But in terms of what we're focusing on at Tulane today, it's a lot about digital health on commercial devices and readily available devices for the patients. And I think of it kind of like how you could go to the hospital when you have a fever and then pay $500. But instead, you could also just go to Walgreens and give a NyQuil and then feel better the next day. So I'm not sure if this is the best analogy. But in terms of this, I think there are a lot of tools available nowadays where AI can help patients see some of these risk factors right away and communicate with their physicians better instead of having to schedule an appointment and go to the physicians right away. I agree with all of the comments that have been previously made. I think we have to look at AI in this particular perspective. I think we're early on in the journey. We're kind of driving a Model T right now, and we have electric cars that are 100 plus years later becoming the manner by which we do it. And being in that early phase, I think we have an awful lot of opportunity to see where the weaknesses are, where the gaps are, and develop AI programs to fill effectively those gaps. So as others have said, patients get effective care, and they get it actually in a cost-effective manner because I think that's going to be a very important component going forward. We already know, and you mentioned, Kevin, nicely about physician burnout and overwork. And I think it's not just physicians. It's the entire clinical team that is short-staffed and oftentimes frustrated and burned out. And we know right now that there are, like you mentioned, Elaine, there are things like the EKGs, which are set up initially with a reading that can then be modified. And we know that the diagnostic capability of these devices using AI to read reports from implantable devices and read wearable reports and read imaging reports are equal to or better than many physicians. So we have to figure out how to integrate that so we can free up the physicians, free up the other clinicians so that they have time to actually communicate to the patients. Because one of my biggest concerns is with all the work that is out there and the demographic changes which are occurring and the large number of patients suffering from heart disease is that folks don't have the time to really true to talk to the patient, find out what are their goals in life, what is important to them, and put it into that perspective. So I look at AI as a tool, a very, very important tool, maybe the best tool that we've ever had in our field, but we have to look at it as a partner, as a tool, and together by putting those things in place, I think we can get to the next best place. So in our organization, we're using it in somewhat of a minimalistic way at this particular point, helping with scheduling, helping with some diagnostic considerations, and trying to limit the amount of burden that the docs are truly dealing with. But I see immense potential here, but I think if I had to make one statement, I would say What we need to do is to use that Latin phrase, carpe diem. We need to seize the day and figure out where we want to go. Define what is important, prioritize, and move effectively in that direction. So a lot's already been said. I'm going to break it up into four parts, and I think when you look at AI, you can kind of look at it as predictive analytics, or analytic AI, generative AI, robotics, virtual reality. And we've been dabbling in each one of these aspects of care across multiple disease states, and I think the commonest ones that many of us encounter are atrial fibrillation and heart failure. So just to give you an example, for atrial fibrillation, we've done a fair amount of work where we used ECGs to predict AFib in the future, and work at our place has suggested that you can predict it with a certain accuracy five years from now. At the same time, there's work from our hospital that is actually, from a patch monitor, you can predict which patients are going to develop atrial fibrillation in the next 13 days, and not only that, even predict which patients are going to develop ventricular tachycardia in the next 13 days with an AUC of 0.92. So pretty darn good. Those are investigational still, but they're around the corner, I think. They need to be validated pragmatically in some clinical trials, but they're around the corner. And then not only that, we're using cloud-based algorithms off the Apple Watch to monitor for the QT interval. Again, investigational, but it's actively happening in all of our centers, and it's only a question of time when many of these inpatient situations will become outpatient monitoring and home-based care using conventional variables. So we're getting there, we're moving in that direction, and I think some of that practice is getting into our daily lives. On the robotic side of things, I would say augmented reality. So Jen Silva here founded a company called SentiAR, and we are using augmented reality while doing AF ablations where we can electroanatomically pull in the, sorry, pull in the electroanatomical map holographically and actually move your catheters in a personalized way inside the heart. There are forms of AI that are actually already finding their way into clinical practice investigationally, but soon will become a regular form of our day-to-day practice. And the same thing with heart failure. I know I won't take too much more time, but I think in heart failure, right from the diagnostic component, from the predictive component, and the treatment strategies, you can only imagine that self-management approaches using generative AI where patients can actually talk to the data sets like a real person is, again, happening around the corner. We're using generative AI in our center, just like Kevin mentioned, to help us with DACs or a bridge, write our notes, or through chatbot AIs actually help create and construct a response to our patient's email. This is investigational, and then that can be overread by the nurse and sent forward to the patient. It's relieving us of a lot of burden right now, so certainly, a lot happening in the space. Go ahead. So, from the other perspective as partners to clinics, when you're talking about how the clinic is changing and where it's going, we're helping with remote monitoring of devices and sort of optimizing the device clinic, but as Dr. Manzi said earlier, we're still in the realm of 90% about technology and operational efficiencies and 10% AI. It's still 90% of the time it's about curating data and fixing the system and making it easier, reducing burnout, but I think more important sort of in this respect where the clinic is going is to think about the philosophy and the vision, where we can go, and when I co-founded in 91, the ideology or the idealism was pretty simple. We need to take the doctor back to the center of patient care. The physicians have been disintermediated over the last 30, 40 years from patient care. It's become a lot about administration, insurance. It's become very difficult. It's about RVUs and Q2 and Q3, all these hours, so how do we do that? The way we conceptualize this, we wanted to create these tools and applications and augmented intelligence and mathematical modeling, but the idea was to sort of go back to how patient care is envisioned from a medical and scientific point of view, so one, you have integration of data, then two, you have sort of delivering that knowledge to the patient and discussing with patient, and ultimately, three, negotiating that patient care with all the participants, so our idea is the first sort of step in truly internalizing the value of AI is to do a much better job at integrating data so that you can create sort of this concise representation of information that is found out there in vast amounts of research and data and history and sort of different knowledge across hospitals and health systems and make that palpable, make that sort of digestible for the physician, so that's the first integration. The second, I think, what's going to be much harder is to also have the physician deliver this knowledge to the patient, so in other words, for example, explain hazard rate and probability and Bayesian framework to a patient, which is not going to be easy because math is just not that easy, and then ultimately, you empower the physician and the patient together to negotiate patient care with CMS, with other payers, with hospital systems, and so on and so forth, so I believe, you know, as the esteemed physicians here talked about some of the sort of low-hanging fruit in terms of how AI is being used, ultimately, the goal here is to take all this knowledge, all this information, all this power, and put the physician at the center of patient care so that we can go back to the way physicians had this power to decide about how to treat patients. That's terrific. I guess what I'm hearing is that, you know, the way I always think about, like, technology adoption is it's really about technology but also about people, right? If technology is like 10x better, it's like a no-brainer, you know, if I could just walk in and talk and, you know, create a note, adoption, right? The problem is, like, that 10% better technology, right, like, it's a little bit better and, like, you know, and then it becomes, like, is it seamlessly integrated in my clinical practice? Is it, you know, do I need to get an extra notice, am I getting an extra, you know, alert to my email? Well, you know, it can lead to all sorts of other things, so, like, integration also kind of, I think, relies a lot on a lot of things that you described. It has to be seamless, especially if it's not a 10x better technology, right? So I want to kind of double click a little bit on what you mentioned about burnout, and, you know, whenever I talk about burnout, number one cause of burnout, surprisingly, is technology, right? And we're talking about here about technology causing less burnout, which seems, you know, as a clinician doing Epic every day, I am very suspicious of this, so can you convince me that this is going to be better for me, Elaine? I think it's a catch-22, I think, all of what my panelists, co-panelists are saying, and I think it's so interesting, you know, from the last speakers of the panel, what they're saying here is, well, the last part is, AI can do a lot of complex things, diagnose, ECG, look at the medical records, but what things can it not do? You can hear our panelists talk about what is taking our time, is talking to our patients, what can it not replicate is trust and relationship in the physicians and providing care, you know, and it's a catch-22, because the more sensors, the more data that we're taking, the more we have to explain the patients that we're finding, you have hypertension, you have AFib, you have heart failure, you need to do this, this, this, and this, and to educate them about all these things, for them to trust us that this is what they need to do to improve their healthcare takes a whole entire, like, long list of things. So I think that it's good in diagnosing all these things, and it will allow us to provide better care, but then it helps us realize that we need to do a better, find a better way to have our patients work with us to improve their healthcare. So until AI can help with that also, this is why I say it's really important about the user interface, for them to understand, and especially when we're dealing with patients who are in their 80s or over 60s, you know, if it's AFib, there's a lot of distress in technology and, or literacy in digital health. I think that's the bigger problem, not necessarily distress, but not knowing how to use it that might lead to some distress that ends up being a complicated sort of catch-22. I guess what you're saying is, like, being a physician is a set of tasks, right? And then if you unbundle it, there's a lot of tasks that, you know, chasing charts or doing RC, revenue cycle management, that can be outsourced, so we have more time for things that we can do. Is that kind of what you're alluding to? Yeah, absolutely. I mean, just to highlight what Jag was saying, for the HRS, we had a panel of articles that was published in Heart Rhythm just before this conference and I encourage everyone to take a look at it and we talked about the digital dashboard what would be best and very simple like for digital dashboard some things we said well the most important thing is like for me to contact the patient so just having their contact information like their phone number on the digital dashboard pulled up immediately if it's a red flag would make my life easier rather than go to EMR and search you know what is the best way to contact the patient so I think those are ways to help efficiency or implement all of these algorithms for us to deliver better care yeah I agree I think you know I think everyone's gonna have a healthy amount of skepticism early on and it you know look it's gonna require some investment because we have to build platforms that will allow AI to do what it does best right and that's gonna take time to build those things whether it's you know ways to and this is real like we I'm sure many of us feel it now like the sensors and wearables are incredible we get great data it's a great way for patients to be affirmed or you know brought in if there are concerns that you see but it's a lot of time and investment and so if we can create platforms and AI is able to process that data in a reliable way it can make things a lot easier for us and take you know and I think the way you said it was really good like we do a lot of things in our capacity in caring for patients and a lot of it takes a lot of time I mean how much time do you spend with a new patient trying to track down medical records trying to get a nice summary of you know why are this person here to see me what has happened how many ablations have they had where are they in the treatment process how many you know any rhythmic drugs that they've been on right and so it just think about the ability to be able to synthesize and get that done for you so that you read a nice summary of what's happened and then now you're ready to go see the patient right so AI as like a data information specialist at your side so you can you know spend more time ablating maybe potential absolutely well I don't really burn out from like patient care my computer burns out but I do think that the burnout really comes from the abundance of data not the AI itself and you know and so much of the AI models now are still like insistently like decision demanding then rather than decision supporting for the physicians and I think that's really the core of the AI development that needs to happen to optimize the physician workflow I think dealing with inefficiencies and redundancies makes it hard for all clinicians and leads to burnout so I would say from my perspective there are a couple of things that I think are necessary as we continue to develop AI one is reliance and excellence it needs to be really accurate so that we have confidence and I mentioned earlier I look at it as a tool and I look at it as a partner of mine so that when we get results we can rely on the fact that they're accurate that leads to the second component in my idea and that is expediency we need to be able to communicate to patients get that data to patients effectively and we need to be able to get it to them in terms that they understand a PhD you know engineer is going to be a lot different than you know someone who has English as a second language and minimal education we've got to be able to look at it as a partner who can help with education as well and I think that is really key because then when the patient comes in to see us we know where they are they have learned more about what is going on and what can be beneficial and I think last but not least we need to make it so that everything can you know connect and work together many times what happens at institutions is and it's through no negative fault on the part of the doctors or the administrators but people function in silos and everybody's so busy is that communicating is very difficult so if your job is to do X and your job to do Y and my job to do Z we can compartmentalize it and can have ways where AI can help communicate effectively so we're not creating redundancies but we're making sure that things do react and get done appropriately and I think being able to communicate to patients about results and being able to move it in that direction would be helpful so I think it's important that when we're talking about AI out here where I'm guessing we're talking about generative AI and and the role of generative AI and not you know the analytic and the machine learning end of things which have different connotations associated with it I think the generative AI I think it's important also know that utilizing that in clinical decision-making is no entry zone at this point in time because there are enough issues with confabulation the hallucinations and inappropriate and incorrect advice that can send you on a wrong train so I don't think it's going to help out there immediately obviously when we start training you know curated data sets with limited language models and specifically that's a whole different scenario where we can start using generative AI to to help us with with managing our patients but in terms of you know enhancing efficiency I think what you said Hamid is really spot-on it has to be seamless has to be a part of your workflow if it's not a part of your workflow then it's not going to work point number two is I think the problem is that as we use generative AI to you know create our notes and save time to summarize our charts and save even more time our clinic visits from 20 minutes can become seven minutes is that going to mean that that efficiency is going to lead us to see more patients and what we thought we were using generative AI to enhance the humanism in in cardiovascular care or medicine as a whole where you can spend more time face-to-face with the patient now is being replaced by additional visits and I think that's something we as a community really need to be you know sensitive to and prevent you know that that slippery slope of just making efficiency into just seeing more patients and not about the patient itself with that I'll pass it on yeah so in the beginning technology was built by experts to be used by experts and then Steve Jobs came along and said listen we got to make it simple so I think the solution is you need a true partnership between technologists and mathematicians and physicians and if that were to be let's say a 400 meter Olympic run I think we just got off base and we had a false start and I say this because health care is the second slowest to adopt new technology after the government you know if you look on the government as an industry so I think what we need to do is this partnership between technologies and mathematicians have to happen in a way that information has to be delivered in simplistic form however the drill down has to be available there for the physician in an easy enough format so that the trust is built between what the mathematicians and technologists are providing what the physicians are consuming so dr. one said it right the hardest part I think the challenge where we're far from even conceiving it right now is how do you replace if you will or replicate the relationship between physician and patient I think until we have true AI at least that's gonna be very difficult but we don't need to shoot for that I think we need to just allow more time so what Jack was saying instead of having more patients every seven minutes you spend more time with the patient to deliver the knowledge and to negotiate the care while the delivery of information from the system comes from an explainable AI math technology model that allows the physician to sort of from time to time poke and see let me see this sounds suspicious so you make it simple enough but you don't make it a black box I think the biggest problem we've had is that when AI started medicine it started as a black box I was at the European Cardiac Congress in Warsaw in June and there was someone who was presenting a model that worked really well for defining sort of all these arrhythmias and when asked sort of how does it work I really didn't like the response he said well listen you know done it so many times it works it's tried it's got FDA approval and it works I don't think there's gonna be enough because I know the physician is not just inquisitive but passionate and caring about what decision they're making about the patient so can it help in decision-making absolutely but it needs to develop this trust between I call it machine and physician but really is between the people behind the machine and the physician sounds amazing guy you guys persuaded me that it is you know make me more productive it's faster I'm gonna have less burnout I'm gonna have lots of time to go home on time so why are we using it all the time what's stopping us what are some barriers that you can think of maybe which why aren't you using it all the time in your practice we need the audience to help us we need innovators to help us I think that's what the panel is saying we need shortcuts to make it in our clinical workflow I think that all of us could just look at the number of apps we have on our phone right for health care delivery we're asking for consolidation for sort of easier access to bridge the gap so I think we're I think all of our panelists are saying we're just in the beginning and we're thankful for HRS to bring innovators because obviously there are a lot of gaps and a lot of needs I think those are the limitations right now and I just want to echo what the previous panel said about the black box I mean you and I and Jag and other people in the audience if you're an engineer is we're scientists you want to understand why does it work and understanding the what's the mechanism inside the black box will help us further refine tune the algorithm and further improve care so I think that also could be helpful instead of grasping on an unknown of why they're such deliverables but I think we're in the beginning we're making good headway and I think the the slope is going very very quickly the acceleration and I think working with the audience here with the innovators and inventors and trying to improve the implementation of this will definitely leave us the next future the bring it closer to a reachable goal for us so a lot of you know making the model more explainable and making it seamless in my clinical practice so Kevin maybe that you know one thing I wanted to ask you specifically about it you know that the scenario being I develop this AI algorithm I give it a goal I'm gonna deploy it and somehow in the middle I fail to follow up to see if the goals are met and then AI kind of drifts and starts doing things that I didn't want it to do is that am I too pessimistic about it am I freaking out too many too many sci-fi movies and implications for that in healthcare right like if you optimizing for 30-day readmissions am I gonna like you know poison everybody and no one gets readmitted right no no I think I think you really raise a really important point because sometimes in in the process of development sometimes you can lose track of the outcome of significance right and so I think it's really careful that you know we have core principles as we're developing these algorithms using machine learning in either more more complex neural networks like you know deep learning that will allow us to understand things better every step of the way we've got to ask ourselves are we holding true to our core tenants of what we're doing right because that's where we go wayward if we're not doing that and holding ourselves accountable so first is it is what we're doing transparent and accountable right and that's how we're gonna build trust with our patients and that's how we as clinicians are gonna embrace this more if we can answer that question we have to also ask is it is it trustworthy is it fair is it equitable is it treating all patients the same or is it understanding that the root of equity is we all come to this at different points with different challenges in terms of thinking about our health care and our lived experiences so we've got to ask that cost-effective and I think the point that Jag raised about you know if we become more efficient what does that mean for us right we're going through that with PFA right now and people are running around saying yeah I'm doing AFib in seven minutes why are we doing that like what are we trying to accomplish with that right to get our RV use cut more to get less things paid for we're already dealing with that and so as we tout efficiency as a goal here we've also got to think about what that means for reimbursement we have to have our payers at the table we have to have our patients at the table to say hey you know I don't want my visits to be seven minutes now like I want my doctor to talk to me in ways that I can understand using this tool to support it where it can that makes sense and so we have to do all those things so cost has got to be at the forefront of this conversation as well and I'm gonna shift gears a little bit because I think this is important to talk about as we think about machine learning and as we're putting data in I'm just gonna say a very practical want that I have I want to know how to ablate persistent atrial fibrillation I don't want a different technique that works sometimes and sometimes it doesn't I'm putting alcohol in the vein of Marshall one day I'm isolating the posterior wall one day I'm losing complex fractionate electrograms one day like I just want to know how to ablate a fib so that my patient will have a great outcome right and so asking those pragmatic questions that will help us take care of patients and hence I know my own personal sanity with what to do I mean but these are the kind of questions that we need to really move the field forward and it's done through partnerships so that the engineers and the mathematicians that we're working with are helping us answer the right questions and they understand what this and how this ties back to patient care and perhaps efficiency should not be our you know the human good that we're optimizing for all the time right there's other things more important efficiency maybe I want to piggyback on a comment that Kevin made you know one of the major issues always with adoption of these models is data right yeah we have lots of data but most of it is garbage all right so can you maybe a little bit touch about touch on like importance of data as one of the limiting key factors for getting these models to act properly and and what are the implications of that for compute yeah in two minutes yeah that's a whole time so you know obviously the quality of the data is really important when you think about like signals of ECGs and how clean they are but also in terms of the population it's representing so it may also cause disparities if you're with poor sampling and a lot of these models performing poorly like not being generalizable is because of this specific part and and you know one of the things that was whether we talked about this about like explainable models and stuff I'm actually a little bit like against this idea of all the all the clinical models being fully explainable I've read like a bunch of papers on like ECG AI models and their saliency and stuff and it goes into like one beat and then like has this heat map of like you know what it's looking at in a PUA or something like this but there's also so much about like time you know base like the frequency of like what's the you know what's the relationship to this peak and that peak and this kind of like time frequency events actually cannot be displayed in in a saliency model that was designed for images so like a lot of we're actually explaining it wrong and so much of it is happening because that's how someone else did it in a different field of AI actually like you know we ask such a PT something and then we don't really like think about like why it answered this way as long as it's answering it right all the time and I don't know maybe it's a different perspective as an engineer but if it's right all the time then do we really need to know when we don't really like have a proper way to explain it and these papers are being published everywhere in the biggest journals but I don't really I haven't really seen like many papers like addressing saliency as a you know it's a time series model being explained through images it's kind of weird but yeah that's I understand exactly what you're saying it's that the Greeks were always very hesitant of anyone who tried to explain everything right so maybe there is something there maybe Tom you can maybe touch a little bit on like how you are like what are the actual payment and reimbursement issues are stopping you from adopting these in a health care system like yours I think that's a very good point right now you know there is no specific payment for AI as we all know however we've got to be able to have administrators and leaders who are you know facing very significant economic problems understand that it can be beneficial and I'd be frank I mean I don't think every institution can lead this I think we need to have certain institutions that have the expertise scientifically have the expertise engineering Lee have the operational connections to be able to drive something prove that it can lead us to where we need to be clinically and then show how it can create value that is by improving outcomes at a lower cost I don't think we can do it across the board and I think we need to be focused different institutions function differently but if one or two can get together and show how that can be done and then demonstrate benefit we can broaden it so for I'll just give an example like guideline directed medical therapy for patients who have left ventricular systolic dysfunction before they get a defibrillator we all know that the vast majority of patients getting an ICD are not on guideline directed medical therapy at the maximal tolerated component so putting in a program that can do that they can work with the heart failure general cardiology or EP docs and get them to that level and showing that maybe some patients don't need these devices or if they do they truly need it and they've been maximized as much as possible so I think we need to have institutions function as a as a cow that is a coalition of the willing work together to build a program then try it from your institution to broader institutions because private practices large institutions academic centers small and large are all going to have different approaches so I would say start small build something have metrics to show it works demonstrate that those metrics add value and then broaden it and see where it goes and so it may be value-based models could be a forcing function for adoption and on a large scale yes jag you know it's so much of you know being a doctor requires tacit knowledge you know you have to kind of like be at the presence of the patient interpret not only words but images and data but how you're interacting their overall interactions with you how do you see that hindering adoption of these models or is this something that's going to you know am I going to not want to use it because I'm afraid that this is going to you know interrupt that sacred relationship or or there's something I'm missing there yeah that's a good question I wish I had 15 minutes to talk about that because that's an amazing topic to talk about I think it's really important to recognize that you know the massive processing power we have the the ubiquitous data we have and and the unlimited connectivity we have is going to change the way we receive and deliver care for sure we have to recognize that it's going to be a merge or of both the digital touch and the human touch and medicine is going to transform out there we have to however self-impose upon ourselves to keep technology tamed that it doesn't overwhelm us and overtake that human bond that we have with our patients so it's really I know it's an important question and you have just 25 seconds and I want Blair on to have the last question but happy to talk about that more later on we definitely have to talk about that some more so what are some technological issues when it comes to adoption of these take these on a large scale if you could specifically touch on the the compute limitations both on the training and also on the inference side I think the biggest limitations you could say a technical level is access to data which intellectual physiology is much more viable but in other fields it's a little bit more difficult but I think the reason AI is slow to to be adopted in healthcare is because of sort of one factor that is much more significant here than any other field and that is ethics you know you can use AI in finance and train if it doesn't work you throw it out try another model worst case scenario you lose money usually other people's money unfortunately but in healthcare you can't try and kill a few patients and say okay I'm going to try with the other one so I think that is one big consideration just one sort of final replication to a channel set about explainable models it is absolutely true you need sort of multiple dimensional graphics to explain things like k-neighbors and sort of some of machine learning techniques and the time series dependence when you generate the signal so I don't think we need to sort of get to that level of explainability where you can literally sort of recreate the computer and the and the binary code and all that stuff but I think there needs to be enough comfort like it is for a mathematician when they drive through a proof that I can use a theorem that has been proved and I know it's been proved and if I really wanted I could go down and understand the theories I don't need to necessarily know the guts of the system but we need to have models that are sort of comprehensible at the high level for physicians and mathematicians need to understand what they're doing it goes into a dimension beyond our sort of human intelligence because we're talking about big data and all kinds of processing that has these dependencies that are difficult to follow but ultimately I think ethics explainability and a strong partnership between mathematicians technologists and physicians is what's going to catalyze this adoption of AI and in my opinion I think we're probably five years away from when we're gonna have a much smarter hospital with AI. Thank you so much this was a lot of fun I learned a ton please I wish we had some more time please go ahead and read the articles that are published in Heart Rhythm Journal there's a link in the description of the session I want to thank our panelists this was fantastic I'd love to be able to continue this conversation on the sidebar over coffee or drinks and thank you again

AI in healthcare

clinical practice

diagnosing

patient care

clinician burnout

ethical considerations

data quality

collaborative refinement