Mamba-Based Multimodal Learning for Automated Tumor Characterization and Clinical Decision Support in Precision Oncology
Keywords:
Mamba, multimodal learning, precision oncology, clinical decision support, tumor characterization, governance, health equity, deploymentAbstract
Precision oncology requires the synthesis of heterogeneous clinical evidence, including radiological imaging, digital pathology, genomic signatures, laboratory trends, treatment histories, and longitudinal outcomes. Although multimodal artificial intelligence has demonstrated promise for tumor characterization and clinical decision support, many existing architectures remain computationally expensive, difficult to govern, or poorly aligned with real-world clinical workflows. Mamba-based state space models have recently emerged as a compelling alternative because they can model long-range dependencies with linear-time sequence processing while preserving selective information routing. This paper examines the system-level implications of Mamba-based multimodal learning for automated tumor characterization and clinical decision support in precision oncology. It analyzes architectural trade-offs among transformers, convolutional networks, and selective state space models; discusses data infrastructure, interoperability, and secure deployment; and evaluates human factors, robustness, fairness, sustainability, and policy considerations. The central argument is that Mamba-based multimodal systems can reduce computational barriers to longitudinal oncology intelligence, but their clinical value depends less on isolated predictive accuracy than on integration with tumor board workflows, regulatory oversight, continuous monitoring, and equitable data governance. The paper concludes that precision oncology needs hybrid, auditable, and lifecycle-managed multimodal platforms rather than single-model solutions.
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