Explainable State Space Models for Automated Cancer Detection and Clinical Decision Support
Keywords:
explainable artificial intelligence; state space sequence models; clinical decision support; cancer detection; multimodal medical imaging; algorithmic governanceAbstract
Automated cancer detection has become a central application of machine learning in clinical imaging, yet the translation of high-accuracy models into trustworthy clinical decision support remains constrained by interpretability, computational complexity, and governance requirements. This article examines the emerging role of explainable state space models as a system-level alternative to attention-based and convolutional architectures in oncological imaging pipelines. State space formulations offer efficient long-range sequence modeling, which is relevant for volumetric radiology, digital pathology, and multi-view cancer screening where inputs contain extensive spatial context. However, efficiency alone does not satisfy the epistemic and regulatory expectations of clinical oncology. The paper develops a multidisciplinary analysis that connects architectural properties of state space models to clinical explainability, deployment infrastructure, robustness, fairness, and postmarket governance. It discusses structural trade-offs between the hidden state representations of state space models and the need for clinically meaningful explanations, contrasting these models with established interpretable and post hoc explanation paradigms. The analysis considers data provenance, model reporting, workflow integration, and sustainability in health systems. It also addresses failure modes related to population bias, domain shift, and institutional accountability. The contribution of the paper is a systems-oriented framework for evaluating explainable state space models not as isolated algorithms but as components of a broader socio-technical infrastructure for cancer detection. The conclusion outlines future directions for hybrid interpretable architectures, regulatory alignment, and longitudinal safety assurance in clinical decision support.
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