迈向医学影像分析中临床可用的基础模型：适应机制与部署权衡
Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs
AI总结：
本文探讨了医学影像分析中基础模型的适应机制与部署权衡，分析了不同适应策略对临床鲁棒性、校准和监管可行性的影响，为设计符合临床需求的基础模型系统提供指导。
摘要：
基础模型（FMs）在跨医学影像任务中表现出强大的迁移能力，但其临床实用性取决于预训练表示如何适应领域特定数据、监督制度和部署约束。先前的综述主要强调架构进步和应用范围，而适应机制及其对鲁棒性、校准和监管可行性的影响仍缺乏系统性结构。本文介绍了一个以策略为中心的框架，用于医学影像分析中的FM适应。我们将适应概念化为预训练后的干预，并将现有方法分为五种机制：参数级、表示级、目标级、数据驱动级和架构/序列级适应。对于每种机制，我们分析适应深度、标签效率、领域鲁棒性、计算成本、可审计性和监管负担的权衡。我们综合了分类、分割和检测任务的证据，强调适应策略如何影响临床相关的失败模式，而不仅仅是整体基准性能。最后，我们探讨适应选择如何与验证协议、校准稳定性、多机构部署和监管监督相互作用。通过将适应重新定义为在临床约束下的受控表示变化过程，本文为设计鲁棒、可审计且符合临床部署需求的基础模型系统提供了实用指导。
英文摘要：
Foundation models (FMs) have demonstrated strong transferability across medical imaging tasks, yet their clinical utility depends critically on how pretrained representations are adapted to domain-specific data, supervision regimes, and deployment constraints. Prior surveys primarily emphasize architectural advances and application coverage, while the mechanisms of adaptation and their implications for robustness, calibration, and regulatory feasibility remain insufficiently structured. This review introduces a strategy-centric framework for FM adaptation in medical image analysis (MIA). We conceptualize adaptation as a post-pretraining intervention and organize existing approaches into five mechanisms: parameter-, representation-, objective-, data-centric, and architectural/sequence-level adaptation. For each mechanism, we analyze trade-offs in adaptation depth, label efficiency, domain robustness, computational cost, auditability, and regulatory burden. We synthesize evidence across classification, segmentation, and detection tasks, highlighting how adaptation strategies influence clinically relevant failure modes rather than only aggregate benchmark performance. Finally, we examine how adaptation choices interact with validation protocols, calibration stability, multi-institutional deployment, and regulatory oversight. By reframing adaptation as a process of controlled representational change under clinical constraints, this review provides practical guidance for designing FM-based systems that are robust, auditable, and compatible with clinical deployment.
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