Dr. Hee-Cheol Kim Ph.D. in Numerical Analysis and Computing Science from Stockholm University, Sweden. He is a senior professor in the Department of Computer Engineering and as the Head of the Institute of Digital Anti-Aging Healthcare at Inje University, South Korea. He is the President of the Korea Institute of Information and Communication Engineering (KIICE) research society and the Co-Chair of the IEEE International Conference on Advanced Communication Technology (ICACT). He also leads the Smart Computing Research Laboratory. His research interests encompass medical image processing, pathology image analysis, natural language processing, computer vision, text mining, bioinformatics, the metaverse, blockchain, and federated learning.
Title: Revolutionizing Healthcare with AI: Innovations in Medical Imaging and Clinical Decision-Making
Abstract: Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This keynote will explore cutting-edge AI techniques applied in medical imaging and clinical decision-making, focusing on deep learning, computer vision, natural language processing (NLP), and federated learning. Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have significantly advanced automated disease detection and segmentation in radiology and digital pathology, improving efficiency and consistency. Generative AI techniques, such as diffusion models and generative adversarial networks (GANs), enable data augmentation and synthetic image generation, addressing challenges like data scarcity and bias. Large language models (LLMs) and NLP techniques play a crucial role in clinical text analysis, aiding in automated medical report generation and disease prediction from electronic health records (EHRs). Additionally, federated learning ensures privacy-preserving AI model training across multiple institutions, fostering collaboration while maintaining data security. This keynote will discuss these AI-driven advancements, explore challenges such as interpretability, bias, and regulatory compliance, and highlight future directions for AI-powered healthcare.