Multimodal data integration is transforming healthcare. Explainability in AI is key to ensuring responsible development and deployment of transformative technologies. 

Learning from diverse experiences in this integrated space makes AI more explainable and accessible—bridging the gap between complex algorithms and practical medical care. Through discussions spanning operations, computation, ethics, and clinical practice, we explore how these technologies can enhance both diagnostic precision and patient outcomes.