M&M: Multi-modal Models and Medicine

Healthcare today stands at the intersection of technology and innovation, driven by diverse data sources—from clinical reports and electronic health records to medical imaging, vital signs, and numerous forms of unstructured data. While deep learning has significantly advanced medical imaging, the vast potential of integrating these abundant, multi-modal data streams remains largely untapped. This integration promises revolutionary improvements in patient outcomes, yet navigating this landscape poses unique and complex challenges due to the fragmented and isolated nature of healthcare data. This workshop addresses the critical questions facing researchers and practitioners: How can we effectively align and integrate multi-modal medical data? How do we tackle safety, privacy, interpretability, and the scarcity of clinically driven benchmarks?