Measuring Cross-Modal Interactions in Multimodal Models

InterSHAP enables precise measurement of how different data sources work together in AI models: The paper introduces a new method that can quantify not just individual contributions from different modalities (like medical images and patient records), but specifically measure how these modalities interact with each other—a capability that existing explainability tools lack.

Current AI explainability methods are insufficient for healthcare's multimodal complexity: Most existing explanation techniques only work with single data sources or are limited to two modalities, rely on labeled training data, and depend on model performance—all significant limitations when healthcare AI systems must integrate multiple data types (imaging, text, numerical data) and provide trustworthy explanations for clinical decisions.

InterSHAP works without approximations and provides patient-level insights: Unlike previous approaches, this method uses the Shapley interaction index to provide exact calculations rather than estimates, and critically, it can explain decisions for individual patients rather than only offering general model insights—essential for clinical adoption where doctors need to understand why a specific recommendation was made.

The method is designed for practical adoption: By integrating InterSHAP with the widely-used open-source SHAP package, the authors have prioritized reproducibility and accessibility, making it easier for healthcare institutions to implement and verify the tool's reliability.

Real-world validation demonstrates clinical applicability: The paper demonstrates InterSHAP's effectiveness on actual multimodal medical datasets, showing it can handle the complexity of real healthcare data and provide the individualized explanations necessary for clinicians to trust and adopt AI systems in practice.