Efficient State Space Models for Real-Time Ultrasound Image Analysis in Clinical Diagnosis
Keywords:
state space models, ultrasound imaging, real-time diagnosis, clinical deployment, medical AI governance, efficient architecturesAbstract
Ultrasound imaging occupies a central role in modern clinical diagnosis because it is portable, low-cost, and free of ionizing radiation. However, the real-time interpretation of ultrasound data presents substantial computational and systems engineering challenges. Clinicians require continuous visual assessment of dynamic anatomical structures, often in emergency or point-of-care settings where latency, memory availability, and power consumption are tightly constrained. Deep learning models have improved ultrasound analysis, yet many architectures rely on attention mechanisms or convolutional hierarchies that introduce significant computational burdens when processing long video sequences. State space models have recently emerged as an efficient alternative for sequence modeling, offering favorable scaling properties while preserving the capacity to capture long-range dependencies. This paper examines efficient state space models for real-time ultrasound image analysis from a systems perspective, focusing on architectural design, structural trade-offs, clinical workflow integration, data governance, robustness, fairness, sustainability, and regulatory implications. Rather than emphasizing isolated algorithmic accuracy, the discussion develops a socio-technical view in which model architecture, hardware deployment, institutional oversight, and clinical accountability are treated as interdependent system components. The paper considers how state space models can be integrated into ultrasound pipelines, what limitations remain, and how future developments may align with responsible clinical AI practices.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
