Speaker: Dr. Pingkun Yan
From: Rensselaer Polytechnic University
Abstract
Despite advancements in AI models for medical image analysis, ensuring their trustworthiness remains a challenge. Accuracy, interpretability, robustness, and generalizability are critical for gaining the confidence of healthcare professionals and other stakeholders. This talk focuses on AI model interpretability and generalizability, key aspects of trustworthiness, by exploring the following core questions: 1) Are explainable AI methods reliable, and can we quantitatively measure that? 2) Can we predict when a model might fail to generalize, and how can we enhance its generalizability? 3) How can pre-trained medical Vision-Language Models be adapted to new diseases with limited data? These questions are pivotal in advancing trustworthy AI applications in healthcare.
For more info, please follow this link.
Read More