Mamba-Based Multi-Modal Fusion for Personalized Diagnosis in Precision Medicine
Keywords:
Mamba; multi-modal fusion; precision medicine; personalized diagnosis; state space models; federated learning; algorithmic fairness; clinical infrastructureAbstract
Precision medicine increasingly requires diagnostic systems that can integrate imaging, genomic, laboratory, physiological, and longitudinal clinical data into stable, interpretable, and patient-specific representations. This paper presents a system-level analysis of Mamba-based multi-modal fusion architectures for personalized diagnosis. Unlike conventional attention-centric models, selective state space architectures offer a compelling trade-off between long-range dependency modeling and computational efficiency, but their introduction into clinical infrastructures raises structural questions that extend beyond model accuracy. The paper examines modality alignment, missing data, temporal irregularity, cross-modal representation bottlenecks, and the architectural implications of linear-complexity sequence modeling in hospital settings. It further analyzes governance, deployment, robustness, fairness, and sustainability as first-order design constraints rather than post-hoc considerations. Drawing on cross-domain evidence from medical imaging, oncology, critical care, and federated learning, the discussion reframes personalized diagnosis as an infrastructure problem in which model architecture, data governance, and clinical accountability must co-evolve. The paper argues that Mamba-based fusion can support precision medicine only when embedded within a broader socio-technical framework that addresses data heterogeneity, institutional variation, regulatory oversight, and long-term operational resilience.
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