Abstract

Artificial intelligence (AI) has shown transformative potential in healthcare, particularly in medical imaging and clinical decision support. However, real-world deployment of AI systems remains hindered by two fundamental challenges: lack of trustworthiness and limited clinical usability. In this talk, I will discuss recent advances aimed at bridging these gaps. First, I will introduce methodologies for uncertainty quantification and out-of-distribution detection, enabling AI models to recognize when their predictions may be unreliable—a critical feature for patient safety. Second, I will also present GlobeReady, a training-free AI platform designed for fundus disease diagnosis that operates robustly across diverse populations and clinical environments without the need for retraining or technical intervention. Together, these efforts demonstrate a pathway toward developing AI systems that are not only technically robust but also aligned with the needs and workflows of frontline healthcare professionals.

About the speaker

Dr. Huazhu Fu is a Principal Scientist at the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore. His research focuses on AI for Healthcare and Trustworthy AI. He has authored over 300 publications in leading venues, with more than 40,000 citations on Google Scholar,  H-index exceeding 90. He has been recognized as a Clarivate ‘Highly Cited Researcher’ and included in the ‘World's Top 2% Scientists’ list by Stanford. He serves as an Associate Editor for IEEE Transactions on Medical Imaging (TMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), and IEEE Journal of Biomedical and Health Informatics (JBHI). He is a Fellow of IET.