Dr. Ramy Shaaban - Best of the Best in Pediatric Surgery 2025

We're gonna 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 Elhaban. Uh, it's, it's an iPad presentation, and, uh, it's embracing the future, the impact and integration of AIML 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 Chapan. Um, I'm 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, an 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, it is happening right now. Um, Take, uh, Google's DeepMind Alpha Fold, for example. Uh, it's solving one of biology's biggest puzzles, uh, predicting protein structures, helping researchers develop new treatment faster. Then there is IBM Watson Health, uh, which analyses 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 effectively. Now, let's talk about large language models, like uh OpenAI's JGBT. 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 GBT 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 it becomes 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 a big question, like how we protect patient privacy. How do 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 uh is prompt engineering, which is now a rising field. Um, instead of, of resisting AI. I think doctors need to understand how to accurately prompt AI to get the best results, that way we treat AI as a tool that assists us instead of 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 Chajity or Del E or Hajian are open, uh, opening new doors to, uh, medical education or research and patient engagement. But again, the goal is not to replace uh healthcare professional, it is to support them. So think of AI as a brainstorming partner, helping to generate ideas and solutions 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, and the answer is no. AI, 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 expertise. Um, now, uh, 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 matters, 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. Rami, Rami, 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 an avatar created to do that. How are we gonna 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, and, uh, by the way, it becomes harder and harder because, uh, 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, uh, it, it's tough now, uh, differentiating between what's AI, what's not AI. Um, so, um, uh, I think it should be disclosed what platform you're using, and 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 Do that. So we are feeding the virtual patient with virtual cases and then make the patient act like they are having that medical case and and then injecting that into the virtual reality. So using local GBTs could be a solution, but I think, yeah, disclosing the platforms that that will be a potential solution as well. Awesome. Thank you, Ray. Thanks, Ray. Thank you. All right, so we have, that was the final presentation. So, um, we are gonna go to the polls now and, uh, let's see here, and this will be the final poll. And then what we're gonna do is, uh, take the winner of this, of the different heats and bring them together. Uh, it looks That, uh Doctor Ryan, uh, is definitely, uh, dominating the, uh, polling here. Um, and I think we could probably even call it, um, so, Doctor Mark Ryan from Scan the Scalpel from IPEC, uh, congratulations on winning this heat. Thank you everybody for coming in.