Vision-Language Mamba Models for Multimodal Medical Image and Clinical Report Understanding
Keywords:
vision-language models; state space models; medical imaging; clinical reports; multimodal learning; health AI governanceAbstract
The convergence of medical imaging and clinical narrative text holds substantial promise for diagnostic decision support, yet it also exposes deep technical and organizational challenges. Transformer-based vision-language models have advanced multimodal representation learning but are constrained by quadratic scaling when applied to high-resolution volumetric images and lengthy clinical documents. Selective state space models, particularly the Mamba family, provide a compelling alternative through linear-time sequence modeling and input-dependent state transitions. This paper presents a system-level examination of vision-language Mamba architectures for multimodal medical image and clinical report understanding. It analyzes architectural choices involving visual tokenization, bidirectional scanning, cross-modal fusion, and report generation. Beyond model design, the paper considers data governance, infrastructure requirements, deployment constraints, robustness, fairness, and regulatory oversight. It argues that efficiency gains must be accompanied by rigorous evidence of subgroup performance, auditability, and resilience to distribution shift. The discussion connects clinical safety requirements with systems engineering trade-offs, emphasizing that multimodal medical models are best understood as components of broader sociotechnical infrastructures rather than isolated predictors. A forward-looking agenda is proposed for integrating state space vision-language systems into clinical workflows in a manner that is sustainable, equitable, and accountable.
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