Dr. Timothy B. Lautz - Best of the Best in Pediatric Surgery 2025

We're going to go on to the last presentation, um, and I have a conflict of interest on this one. This is uh my previous fellow, uh, Rami S Shaban. Uh, it's I it's an ipeg presentation and uh, it's embracing the future, the impact and integration of AI, ML and LLMS in modern healthcare. And Rami is at the Department of instructional Technology and Learning Sciences at Utah State University. Hi, um, my name is Rami Shaban. Um, I am an assistant professor at Utah State University, um, in instructional Technology and Learning Sciences, and I'm talking today about embracing the future, the impact of uh, and integration of AI, machine learning and large language models in uh modern healthcare. So AI in healthcare isn't just a futuristic idea, uh, it is happening right now. Um, take Google's deep mind Alpha fold, for example. Uh, it's solving one of biology's biggest puzzles, predicting protein structures, helping researchers develop new treatment faster. Then there is IBM Watson Health, uh, which analyzes complex patient data, uh, to assist doctors in making better decisions. Uh, these technologies are not replacing doctors, they are giving them superpowers, accelerating drugs drug discovery, personalizing treatment and even handling administrative tasks more uh effectively. Now, let's talk about large language models, like Open AI's chat GPT. These models are changing um, the way medical professionals access information. Imagine being able to get real-time evidence-based insights and quickly summarize medical literature or even receive diagnostic suggestions, all with AI. Chat GPT, for example, is already being used to assist in decision making and making making sure clinicians have the right information at their fingertips when they need it. And machine learning is another game changer. It is already being used in diagnostic imaging and helping doctors detect diseases like cancer earlier and more accurately. Uh, predictive analytics is another area where uh also machine learning um shines. Um, it helps forecast health risks and allowing doctors to intervene with problems um uh when become severe. Um, this means better and more customized care for patients. So, of course, with great power comes great responsibility. So artificial intelligence in healthcare brings up big questions like how we protect patient privacy. How do um, we ensure AI decisions are fair and unbiased? And most importantly, how do we keep the human touch in medicine? So artificial intelligence is a tool, not a replacement for human expertise. That's why continuous education and responsible implementation are key to making AI work for everyone. So, um, uh one thing I want to mention is prompt engineering, which is now a rising field. Um, instead of of resisting AI, I think um doctors need to understand how to accurately prompt AI to get the best results. That's that way we treat AI as a tool that assist us instead of uh a competitor that is coming to replace us. Um generative AI, um, takes things a step further. So this type of AI can create content, text, images, even audio and video. Uh tools like chat GPT or dali or Han are often uh opening new doors to medical education or research and patient engagement. But again, the goal is not to replace a healthcare professional, it is to support them. So think of AI as a brainstorming partner helping to generate ideas and solution faster. So this way you can uh get the most out of it. A common fear of AI in healthcare is, well, it replaces doctors. The answer is no. AI is not here to take over. It is here to assist. Uh think of it like uh having a really smart colleague or who can process vast amount of data in seconds. Whether it's just suggesting diagnosis or summarizing research or streaming uh workflow, AI enhances uh what healthcare professionals can do. But at the end of the day, medicine is about human connection, listening and empathizing and making complex decisions that require experience and intuition. Um so AI is a powerful tool, but it works best when paired with human experts. Um now, let's talk about something that every doctor wishes they had more of time. AI is streamlining administrative tasks like clinical documentation. Imagine having AI generate patient summaries instantly, saving hours of paperwork. This doesn't just help doctors, it improves patient care by giving healthcare professionals more time to focus on what really matter, people. Um, so uh what the takeaway from that presentation, the artificial intelligence, machine learning and large language models are transforming healthcare. They are improving diagnosis, uh personalizing treatment and making the system more efficient, but at the end of the day, they are here to assist, not to replace human uh expertise. The future of medicine is not just AI driven. It is a collaboration between humans and technology. Thank you. Thank you, Rami. Great, great presentation. Ray, Ray, thanks for for uh presenting that. Uh I think we all know that AI is this incredibly powerful uh potentially very impactful tool, but it also comes with some significant risk. And I think that was you giving you that lecture, but we know that you know how to make them without you doing that and just providing the script and having uh yeah having a uh what a avatar created to do that. How are we going to be sure when this information comes out, we know what's real and what's not real? Oh, that's a that's a hard question. Easy question for you. So, and and uh by the way, it becomes harder and harder because AI is just getting better. So, um, I I think it is very important to make sure uh we use um, so um, when creating something like that and dealing with patient information, it is very important to use local um uh generative AI models instead of uh of uh large language models that uh that is just uh sharing information out there because it is um it it's tough now uh differentiating between what's AI, what's not AI. Um, so, um, I I think uh it should be uh disclosed uh what platform you're using and uh um I make sure that you're using that locally in your data. And we are actually doing that now with our team at USU creating a virtual patient connected to generative AI. And we are using local generative AI to uh do that. So we are feeding the virtual patient with virtual cases and then make the patient acts like uh they are uh uh having that medical case and and then injecting that into the virtual reality. So, uh using local GTs could be a solution, but I think yeah, disclosing the platforms that uh that would be a potential solution as well. Awesome. Thank you, Rami. Thanks Rami. Thank you. All right, so we have that was the final presentation. So, um, we are going to go to the polls now and uh let's see here. And this will be the final poll and then what we're going to do is uh take the winner of this of the different heats and bring them together. Uh it looks that uh Dr. Ryan uh is definitely uh dominating the uh polling here. Um and I think we could probably even call it. Um, so Dr. Mark Ryan from Scand Scopo from IPeg. Uh, congratulations on winning this heat. Thank you everybody for coming in.