Test-Time View Selection for Multi-Modal Decision Making

The clinical routine has access to an ever-expanding repertoire of diagnostic tests, ranging from routine imaging to sophisticated molecular profiling technologies. Foundation models have recently emerged as powerful tools for extracting and integrating diagnostic information from these diverse clinical tests, advancing the idea of comprehensive patient digital twins. However, it remains unclear how to select and design tests that ensure foundation models can extract the necessary information for accurate diagnosis. We introduce MAVIS (Multi-modal Active VIew Selection), a reinforcement learning framework that unifies modality selection and feature selection into a single decision process. By leveraging foundation models, MAVIS dynamically determines which diagnostic tests to perform and in what sequence, adapting to individual patient characteristics. Experiments on real-world datasets across multiple clinical tasks demonstrate that MAVIS outperforms conventional approaches in both diagnostic accuracy and uncertainty reduction, while reducing testing costs by over 80%, suggesting a promising direction for optimizing clinical workflows through intelligent test design and selection.