放射学中的视觉基础模型：数据、方法、评估和临床转化的范围综述
Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation
AI总结：
对2017年1月至2026年3月关于放射成像数据训练基础模型的研究综述，涵盖数据、架构、评估等方面。Transformer架构和自监督预训练为主，评估集中在分割和分类。虽有可迁移性，但临床转化因多种问题受限。
摘要：
视觉基础模型（VFM）越来越多地应用于放射成像，但它们的定义、开发和评估仍存在异质性。我们对2017年1月至2026年3月发表的同行评审研究进行了PRISMA范围综述，这些研究描述了仅在放射成像数据上训练的基础模型。纳入67项研究并分为三个方面：数据规模和异质性、架构和预训练可扩展性、下游可迁移性和泛化性。数据集主要涵盖脑MRI、胸腹CT和胸部X光，样本数量从少于10万到数百万图像队列不等。基于Transformer的架构和自监督预训练占主导，评估主要集中在分割和分类，与FUTURE-AI原则的一致性参差不齐。总体而言，放射学特定的VFM显示出有前景的可迁移性，但临床转化受限于数据代表性有限、基准异质性、报告不完整和面向部署的评估不足。
英文摘要：
Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous. We conducted a PRISMAScR scoping review of peer-reviewed studies published between January 2017 and March 2026 describing foundation models trained exclusively on radiological imaging data. Sixty-seven studies were included and mapped across three pillars: data scale and heterogeneity, architectural and pretraining scalability, and downstream transferability and generalization. Datasets primarily covered brain MRI, thoracoabdominal CT, and chest X-ray, ranging from fewer than 100,000 samples to multi-million-image cohorts. Transformer-based architectures and self-supervised pretraining predominated, particularly masked image modeling, contrastive learning and multi-stage approaches. Evaluation focused mainly on segmentation and classification, whereas cross-center, cross-scanner, anatomical and modality-shift validation was inconsistently reported. Alignment with FUTURE-AI principles was uneven. Overall, radiology-specific VFMs show promising transferability, but clinical translation remains constrained by limited data representativeness, heterogeneous benchmarks, incomplete reporting and insufficient deployment-oriented evaluation.
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