Prof. Jungchan PARK

Jungchan Park, MD, MS, PhD, is an Associate Professor of Anesthesiology and Pain Medicine at Sungkyunkwan University School of Medicine, Samsung Medical Center in Seoul, Korea. Dr. Park earned his Doctor of Medicine (MD) and Master of Science (MS) degrees from Hallym University, followed by a residency at Kangdong Sacred Heart Hospital and a fellowship at Samsung Medical Center. Notably, he achieved his Doctor of Philosophy (PhD) in Medical Informatics at Ajou University. Dr. Park's extensive clinical expertise spans the spectrum of patient safety, encompassing activities from operating theaters to outpatient departments. His research is dedicated to intraoperative monitoring and the exploration of postoperative complications. With a rich background in both clinical practice and academic research, Dr. Park contributes significantly to advancing the understanding and implementation of patient safety measures in the field of anesthesiology and pain medicine.

Machine Learning-based Prediction Models in Surgical Patients: Basics and Updates

The advent of machine learning (ML) has revolutionized the landscape of healthcare, offering unprecedented opportunities for predicting patient outcomes in surgical settings. This lecture explores the fundamental concepts of machine learning and its applications in predicting surgical patient outcomes.

Beginning with an elucidation of core ML principles, the discussion delves into previous studies that have harnessed ML techniques to enhance surgical precision and patient prognostication. These studies have demonstrated the potential of ML in optimizing surgical techniques, improving postoperative recovery, and personalizing treatment plans. From predicting surgical complications to optimizing resource allocation, ML has proven to be a powerful tool in augmenting the decision-making process for healthcare professionals.

Despite the promising advancements, challenges persist within the realm of ML in medicine. Ethical concerns, data privacy issues, and the interpretability of complex ML models pose significant hurdles. The lecture addresses these issues, shedding light on the delicate balance between innovation and ethical considerations in the development and deployment of ML-based prediction models for surgical patient outcomes.

By critically examining the current state of ML in surgical contexts, this lecture aims to provide a comprehensive understanding of the potential benefits and pitfalls associated with these technologies. As we navigate the intersection of machine learning and surgical patient care, it is crucial to foster a nuanced appreciation for the ethical, technical, and societal implications that accompany the integration of ML into the medical domain. This knowledge will empower healthcare professionals, researchers, and policymakers to navigate the evolving landscape of ML applications in surgery responsibly and ethically, ensuring that advancements in technology translate into tangible improvements in patient outcomes.