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Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking
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DOI：10.1038/s41467-026-74126-5.png)
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Abstract
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Integrating diverse biomedical modalities is essential for robust healthcare insights, and graph-based models are increasingly used to capture complex relational structures. Yet, their clinical translation hinges on interpretability. This review surveys interpretable graph-based models applied to multimodal biomedical data, highlighting dominant trends in disease classification, static graph construction, and post-hoc explainability. We categorize explainable artificial intelligence (XAI) techniques, benchmark SHAP, saliency, sensitivity, and graph masking on Alzheimer’s disease data, and reveal complementary strengths. A development flowchart and future directions, such as dynamic graphs, knowledge integration, and LLM-based explainability, position this work as a key reference for trustworthy biomedical AI. The integration of different biomedical modalities in clinical settings is challenging because of limited transparency and explainability. In this review the authors survey and benchmark interpretable graph-based models applied to multimodal biomedical data.
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