Towards Clinically Applicable Medical AI Systems in Open Clinical Environments

As artificial intelligence (AI) continues to revolutionize healthcare and medical image analysis, ensuring safe and effective AI deployment in open clinical environments has become paramount. However, many existing medical AI methods prioritize model performance, focusing on achieving higher accuracy rather than clinical applicability. In this talk, I will present our works on building clinically applicable deep learning systems for medical image analysis, emphasizing domain generalization, continual learning, and multi-modality learning. Our aim is to inspire the development of more reliable and effective medical AI systems, ultimately enhancing patient care and outcomes. Additionally, I will discuss up-to-date progress and promising future directions in this critical domain.