(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The proliferation of large foundation models has transformed video object segmentation, yet their direct application to medical imaging introduces a complex confluence of system-level constraints, data sensitivity, and domain-specific reliability requirements. While models such as the Segment Anything paradigm offer unprecedented zero-shot capacity, their computational scale impedes deployment in resource-limited clinical environments and raises concerns about long-term sustainability, fairness, and regulatory alignment. This paper presents a systems-oriented examination of parameter-efficient adaptation strategies tailored to medical video object segmentation. Rather than introducing a new algorithmic variation, we conduct a holistic analysis that spans infrastructure design, modular adaptation layers, memory footprint trade-offs, federated learning frameworks for privacy preservation, and governance mechanisms that ensure algorithmic fairness and explainability. The discussion integrates perspectives from large-scale distributed systems, edge computing, and health policy to illuminate structural interdependencies often overlooked in purely methodological contributions. We argue that sustainable deployment necessitates a multi-objective optimization that balances adaptation fidelity against computational cost, while embedding accountability through continuous monitoring and bias auditing. We further explore how parameter-efficient fine-tuning interacts with regulatory frameworks such as the European Union’s AI Act and the U.S. Food and Drug Administration’s evolving guidance on adaptive medical devices, linking technical design choices to compliance pathways. By synthesizing these dimensions, the paper provides a forward-looking roadmap for the academic community, clinicians, and system architects who must collaboratively steer the responsible integration of foundation models into real-world clinical video analysis workflows.