Artificial Intelligence (AI) has advanced at a rapid pace and is expected to revolutionize many biomedical applications. However, current AI methods are usually developed via a data-centric approach regardless of the usage context and the end users, posing challenges for domain users in interpreting AI, obtaining actionable insights, and collaborating with AI in decision-making and knowledge discovery.

As a visualization researcher, I aim to address this challenge by combining interactive visualizations with interpretable AI, with a particular focus on biomedical applications.  In this talk, I will first discuss the prospects for interactive visualization in the application of biomedical AI. I will then present two methodologies for achieving this goal: 1) visualizations that explain AI models and predictions and 2) interaction mechanisms that integrate user feedback into AI models. I will demonstrate how interactive visual explanations can facilitate AI applications via real-world case studies. Despite some challenges, I will conclude on an optimistic note: interactive visual explanations should be indispensable for Human-AI collaboration in biomedical applications. The methodology discussed can be applied generally to other applications where human-AI collaborations are involved, assisting domain experts in data exploration and insight generation with the help of AI.