Assoc. Prof. Yan Pang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Biography: Yan Pang, Ph.D., serves as an Associate Professor at
Guangzhou University after earning his doctoral degree from the
University of Colorado, USA. Prior to his present position, he was an
instructor at the Metropolitan State University of Denver and the
University of Colorado Denver. His primary research revolves around
computer vision, where he conducts systematic theoretical research and
practical applications, particularly in computer vision, medical image
analysis, on-device models, behavior recognition and analysis,
blockchain, et al. Over the past two years, he has been granted 3
national and provincial-level projects. Dr. Pang has published more than
20 papers in SCI/SSCI indexed journals, including IEEE TMI, IEEE TIFS, TSMCS, TNNLS, TIM, et al., and 20 patents. His significant contributions have been applied
practically in diverse sectors such as medicine, agriculture, and
security, making a substantial impact in their intelligent evolution.
Speech Title: "Advancing Healthcare with Large Language Models:
Applications, Challenges, and Future Directions"
Abstract: Large language models (LLMs) have gained significant attention
for their capacity to understand and generate human language, leading to
increasing adoption in various medical fields such as clinical
diagnostics, medical education, drug discovery, and patient care.
However, despite these advancements, a thorough evaluation of their
development, practical deployment, and real-world impact in healthcare
remains scarce. This seminar offers an in-depth review of LLMs in
medicine, covering essential aspects such as model architectures,
parameter scales, and data sources. We will examine their application in
diverse medical tasks, including improving diagnostic accuracy,
supporting personalized treatment plans, optimizing medical
documentation, and advancing medical research. While LLMs show immense
potential, their integration into healthcare is not without challenges,
including concerns over data privacy, model interpretability, and
inherent biases in training datasets. This seminar will critically
address these issues, presenting a balanced analysis of both the
benefits and limitations of LLMs in clinical settings. Additionally, we
will explore ongoing research efforts to overcome these challenges and
provide insights into the future of AI-assisted healthcare.
Asst. Prof.
Ching-Chun Chang
National Institute of Informatics, Japan
Biography: Ching-Chun Chang received his PhD in Computer Science from the University of Warwick, UK, in 2019. He participated in a short-term scientific mission supported by
European Cooperation in Science and Technology Actions at the Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany, in 2016. He was granted
the Marie-Curie fellowship and participated in a research and innovation staff exchange scheme supported by Marie Skłodowska-Curie actions at the Faculty of Computer
Science, New Jersey Institute of Technology, USA, in 2017. He was a Visiting Scholar with the School of Computer and Mathematics, Charles Sturt University, Australia,
in 2018, and with the School of Information Technology, Deakin University, Australia, in 2019. He was a Research Fellow with the Department of Electronic Engineering,
Tsinghua University, China, in 2020. He is currently a Project Assistant Professor with the National Institute of Informatics, Japan. His research interests include artificial
intelligence, biometrics, communications, computer vision, cryptography, cybernetics, cybersecurity, evolutionary computation, forensics, information theory, linguistics,
mathematical optimisation, natural language processing, privacy engineering, psychology, signal processing, steganography, time series forecasting, and watermarking, within
the scope of computer science.